As part of the Data Infrastructure team you will be supporting mission critical big data platforms, to ensure they are fully performant, reliable, available and secure. We call it data infrastructure engineering, also known as DataOps or Database Administration or SRE.

The role is a mixture of development of tooling and operational support for our platforms, two aspects that go hand-in-hand. It requires attention to detail and curiousity about how the systems work under the hood, and gives you a wide base of skills from low level system tuning to general coding. 

We manage four main storage platforms, namely:

• Apache Solr (~2.2 PB)
• Apache HBase (~450 TB)
• PostgreSQL (~15 TB)
• Kafka (~60 TB)

These platforms are all open source, written in Java, Scala or C, and we maintain in-house builds and patching of them. We use a variety of open-source and in-house developed tooling to manage these services, mostly written in Rust and Python, which run primarily on hundreds of servers in multiple data centres and in the cloud.

We maintain a balance between project work and operational/ad-hoc work for all members in the team, whether they are senior or recent graduates, and your day-to-day work will be a mix of these.

The exact project work will depend on your experience, but we will scope it out collectively to ensure it is both achievable and delivers benefit. Some example projects given to recent graduates are:

• Coding mapreduce jobs to validate data between two multi-TB+ hbase clusters (to run in hours rather than months)
• Building out a python code stack to transparently migrate production clients between clusters (without losing any data or requiring any downtime)
• Building and testing new HBase builds and deploying them live (without putting everyone in Brandwatch out of a job)
• Creating a platform for rolling out our data management systems using Apache Airflow (to stop people re-writing custom job management systems)
• Rolling out a DR solution for hadoop on AWS (while keeping the system performant and not bankrupting the company)

If you're joining the team as a graduate engineer we'll be focusing on developing your knowledge of the storage platforms, how to program against them and how to troubleshoot issues. The aim is that you'd be able to help with the staging support by 3-6 months, and join the rest of the team supporting the production systems in 12 months. You'll also be expected to take a full part in the life of the team and, as your experience grows, start designing the infrastructure we run.

This role is based in Brighton, UK, but working remotely will be possible once the initial 6-12 months of training is complete.

Who we are looking for

In house training for the storage systems and our tooling is a necessary part of this role, but we are looking for someone with a keen interest in large scale storage infrastructure.A successful candidate will be expected to have the following:

• An interest in how computer infrastructure actually works, and a passion for learning
• Interest, and ideally production experience, running storage systems, eg. as part of a selfhosted service, a home lab or as part of academic studies
• Experience with Linux systems administration, including experience of trouble shooting
• Fluency with one or more scripting languages, ideally Bash or Python
• Experience helping your peers
• Pride in the quality of your work
• Experience with any of the following will be beneficial:
  • Development experience, in Python, Java, Rust, C/C++ or Golang.
  • Upstream open-source contributions
  • Academic research in scalability, distributed systems or storage infrastructure
  • Kubernetes experience, ideally running or writing Operators Experience with Docker and with CI/CD pipelines

See yourself at Twilio

Join the team as Twilio’s next Security Engineer, Security Automation on Twilio’s Information Security Team.

About the job

This position is needed to assist Twilio’s Information Security Team operate more efficiently by improving and automating our critical security workflows.

As a Software Engineer, Security Automation on this team, you will participate in all phases of the software development life cycle which includes requirements gathering with security teams, technical design, estimations, sprint planning, coding, testing, deployments and on-call support. You will own, operate and maintain automated workflows that support various security programs including but not limited to cloud security, product security, Governance, Risk & Compliance (GRC)  and enterprise security, employing Agile methodologies to continuously deliver value to our partners.

Responsibilities

In this role, you’ll:

• Develop and implement automated and AI-powered security solutions to protect Twilio's Cloud and Enterprise infrastructure, leveraging data orchestration frameworks and LLM-based approaches.
• Build workflows that integrate structured and unstructured data sources into orchestrated pipelines, enabling intelligent decision-making and risk reduction at scale.
• Design and prototype LLM-driven applications to automate security analysis, incident response, and governance tasks, while ensuring explainability and accuracy.
• Collaborate with cross-functional teams to identify security needs and deliver advanced AI-enabled solutions that address those needs effectively.
• Contribute significantly to defining our security automation and AI roadmap, balancing near-term and long-term business impact with technical feasibility and scalability.
• Own and maintain the operational health of automation and AI-driven systems, ensuring they continue to meet reliability and performance requirements (SLOs and KPIs).
• Support on-call operations for tools, pipelines, and AI models owned by the team.
• Document and share knowledge through clear technical documentation of processes, procedures, AI models, and orchestration systems.
• Stay up-to-date on emerging trends in security automation, data orchestration, and AI/LLMs—and apply them to Twilio’s security operations for continuous innovation.

Qualifications 

Not all applicants will have skills that match a job description exactly. Twilio values diverse experiences in other industries, and we encourage everyone who meets the required qualifications to apply. While having “desired” qualifications make for a strong candidate, we encourage applicants with alternative experiences to also apply. If your career is just starting or hasn't followed a traditional path, don't let that stop you from considering Twilio. We are always looking for people who will bring something new to the table!

Required:

• Minimum of five (5) years of experience as a software engineer developing internal tools and automating workflows at scale
• Proficiency in Python (or another scripting language) for development, testing, and automation of security and infrastructure workflows
• Hands-on experience applying AI/ML models, including LLMs, to automate complex business or security processes and build intelligent solutions
• Solid understanding of software security principles, secure coding practices, and security automation
• Experience with data orchestration frameworks (e.g., Airflow, Dagster, Prefect) to manage and scale automation pipelines
• Familiarity with infrastructure-as-code (Terraform), CI/CD systems (BuildKite or similar), and container orchestration platforms (Kubernetes, Docker)
• Strong problem-solving, communication, and collaboration skills, with the ability to iterate quickly on feedback and work across security and engineering teams

Desired:

• Experience with Security Orchestration, Automation, and Response (SOAR) technologies and practices, with a focus on scaling automated incident response
• Proven ability to design and implement security automation frameworks that enable rapid delivery of reliable, reusable, and scalable automated solutions
• Strong interest in or hands-on experience with building and applying AI/ML models (including LLMs) to automate complex workflows in cybersecurity, compliance, or infrastructure operations
• Ability to bridge AI/automation capabilities with security strategy, driving adoption of next-generation solutions across cloud and enterprise environments
• Passion for continuous innovation in leveraging orchestration, automation, and AI to solve real-world security challenges

Location

This role will be remote, and based in the UK.

Travel

We prioritize connection and opportunities to build relationships with our customers and each other. For this role, you may be required to travel occasionally to participate in project or team in-person meetings.

What We Offer

There are many benefits to working at Twilio, including, in addition to competitive pay, things like generous time-off, ample parental and wellness leave, healthcare, a retirement savings program, and much more. Offerings vary by location.

See yourself at Twilio

Join the team as Twilio’s next Security Engineer, Security Automation on Twilio’s Information Security Team.

About the job

This position is needed to assist Twilio’s Information Security Team operate more efficiently by improving and automating our critical security workflows.

As a Software Engineer, Security Automation on this team, you will participate in all phases of the software development life cycle which includes requirements gathering with security teams, technical design, estimations, sprint planning, coding, testing, deployments and on-call support. You will own, operate and maintain automated workflows that support various security programs including but not limited to cloud security, product security, Governance, Risk & Compliance (GRC)  and enterprise security, employing Agile methodologies to continuously deliver value to our partners.

Responsibilities

In this role, you’ll:

• Develop and implement automated and AI-powered security solutions to protect Twilio's Cloud and Enterprise infrastructure, leveraging data orchestration frameworks and LLM-based approaches.
• Build workflows that integrate structured and unstructured data sources into orchestrated pipelines, enabling intelligent decision-making and risk reduction at scale.
• Design and prototype LLM-driven applications to automate security analysis, incident response, and governance tasks, while ensuring explainability and accuracy.
• Collaborate with cross-functional teams to identify security needs and deliver advanced AI-enabled solutions that address those needs effectively.
• Contribute significantly to defining our security automation and AI roadmap, balancing near-term and long-term business impact with technical feasibility and scalability.
• Own and maintain the operational health of automation and AI-driven systems, ensuring they continue to meet reliability and performance requirements (SLOs and KPIs).
• Support on-call operations for tools, pipelines, and AI models owned by the team.
• Document and share knowledge through clear technical documentation of processes, procedures, AI models, and orchestration systems.
• Stay up-to-date on emerging trends in security automation, data orchestration, and AI/LLMs—and apply them to Twilio’s security operations for continuous innovation.

Qualifications 

Not all applicants will have skills that match a job description exactly. Twilio values diverse experiences in other industries, and we encourage everyone who meets the required qualifications to apply. While having “desired” qualifications make for a strong candidate, we encourage applicants with alternative experiences to also apply. If your career is just starting or hasn't followed a traditional path, don't let that stop you from considering Twilio. We are always looking for people who will bring something new to the table!

Required:

• Minimum of five (5) years of experience as a software engineer developing internal tools and automating workflows at scale
• Proficiency in Python (or another scripting language) for development, testing, and automation of security and infrastructure workflows
• Hands-on experience applying AI/ML models, including LLMs, to automate complex business or security processes and build intelligent solutions
• Solid understanding of software security principles, secure coding practices, and security automation
• Experience with data orchestration frameworks (e.g., Airflow, Dagster, Prefect) to manage and scale automation pipelines
• Familiarity with infrastructure-as-code (Terraform), CI/CD systems (BuildKite or similar), and container orchestration platforms (Kubernetes, Docker)
• Strong problem-solving, communication, and collaboration skills, with the ability to iterate quickly on feedback and work across security and engineering teams

Desired:

• Experience with Security Orchestration, Automation, and Response (SOAR) technologies and practices, with a focus on scaling automated incident response
• Proven ability to design and implement security automation frameworks that enable rapid delivery of reliable, reusable, and scalable automated solutions
• Strong interest in or hands-on experience with building and applying AI/ML models (including LLMs) to automate complex workflows in cybersecurity, compliance, or infrastructure operations
• Ability to bridge AI/automation capabilities with security strategy, driving adoption of next-generation solutions across cloud and enterprise environments
• Passion for continuous innovation in leveraging orchestration, automation, and AI to solve real-world security challenges

Location

This role will be remote, and based in Ireland.

Travel

We prioritize connection and opportunities to build relationships with our customers and each other. For this role, you may be required to travel occasionally to participate in project or team in-person meetings.

What We Offer

There are many benefits to working at Twilio, including, in addition to competitive pay, things like generous time-off, ample parental and wellness leave, healthcare, a retirement savings program, and much more. Offerings vary by location.

Soho, London

Hybrid working: 3 days in the office Tues - Thurs

The Role

We are seeking a Data Engineer to join our Data Team, playing a vital part in building the complex, scalable systems that power our data strategy. You will be at the heart of architectural discussions, influencing system design and delivering the high-quality data solutions that bring millions of fans closer to the sports they love. This is a fantastic opportunity to tackle the challenge of optimising our data platforms for real-time AI/ML workloads, mentor talented junior engineers, and make a significant impact on our business. 

At LiveScore Group, we’re the proud home of three of the most exciting brands in the sports and gaming world: LiveScore, LiveScore Bet and Virgin Bet. A fully owned and operated ecosystem that converges the two worlds of sports media and sports betting. We’re proud of the high ratings for our commitment to excellence and fueling fan’s passion for sport driving us to the top.

We don’t just lead; we innovate. Our cutting-edge products and immersive experiences set the standard, but it’s our people who truly make the difference. Every day, our team embody our values: adaptability, teamwork, a fan-driven approach, and an ever-curious mindset that fuels our ambition. As we scale and continue to create a culture that allows all employees to thrive, we know we need the most talented people with diverse backgrounds, perspectives and skills. If you’re good at what you do, come and join us. The more inclusive we are, the more amazing experiences we can create for our users.

We know that job descriptions can sometimes seem daunting, and you might not feel you tick every box. But, if you’re passionate about the role and have relevant experience, we want to hear from you! 

Key Responsibilities 

• Contribute significantly to critical system design decisions, helping to architect data solutions that support real-time workloads.
• Partner with cross-functional teams to translate intricate business needs into effective and innovative data solutions.
• Mentor and provide expert technical guidance to junior engineers, fostering a culture of continuous learning and growth.
• Design data systems to solve problems and improve reliability & cost-effectiveness. 
• Develop and maintain robust, secure integrations with core internal systems and critical third-party platforms. 
• Implement advanced monitoring and alerting, to ensure high system reliability and minimal downtime. 

Skills, Knowledge and Experience 

• You have expert-level proficiency in a core programming language like Python, Java, or Scala, with an emphasis on AI/ML data processing.
• You possess experience with cloud data services and technologies, ideally within the GCP ecosystem (e.g., Dataflow, Pub/Sub, BigQuery, Vertex AI).
• Experience with Airflow environment management and Docker 
• You can clearly and concisely explain intricate technical concepts to both technical and non-technical stakeholders.
• You have extensive experience designing and optimising both real-time and large-scale batch data processing frameworks.
• You have a proven ability to deploy and maintain data infrastructure using Infrastructure as Code (IaC) principles with tools like Terraform.
• You can demonstrate the application of data governance, privacy, and security best practices in a regulated environment.
• You are a proactive and curious problem-solver, skilled in anticipating and resolving system inefficiencies.
• You work seamlessly with cross-functional teams, translating complex requirements into robust data solutions.
• You take full responsibility for the quality, reliability, and performance of the data solutions you help create. 

What can we offer? 

• Fridays Off: Say goodbye to Friday meetings and hello to a three-day weekend! This benefit is part of a trial, to be reviewed in 2026
• Company Performance Bonus
• Flexible Working Agreements where applicable
• Private Healthcare Scheme + Employee Enhanced Assistance
• Enhanced Family Leave - Maternity, Shared Parental & Adoption Leave: up to 6 months at full pay and 6 months at half pay. Paternity leave: up to 4 weeks at full pay
• Subsidised Gym Membership
• Annual Travel Card Loan & Ride to Work Scheme
• Life Assurance (x3 salary)
• Contributory Pension Plan
• Virgin Family: Giving you access to exclusive Virgin offers and experiences
• Thursday drinks in the office and regular socials

Analyst, Commercial Insights – Services 

London, UK (Collab)

Role Overview 

PlayStation is looking for an innovative, detail-oriented, highly motivated Analyst to join our PlayStation Services team. Services sits within the Global Sales and Business Operations organization that manages the PlayStation Plus subscription business, a key revenue generator and innovator for Sony Interactive Entertainment. This role sits within the Planning and Insights team responsible for leading analytics operational excellence in commercial performance, planning, and strategy.  

This role is responsible for insightful and actionable analytics support of our subscription services from a commercial planning standpoint. You will play a critical role in creating data reporting tools, developing methodologies for measuring and forecasting impact to subscriptions, and delivering analytical insights on the performance of offers & promotions, pricing and experience optimizations.  The work you do will directly impact decisions made about the offers and experience made available to PlayStation Plus members. Effective verbal and written communication skills are extremely important, as s/he will collaborate with key stakeholders across a global organization.  

What you will be doing 

• Support the Services Insights team with analysis of commercial and engagement trends across the PlayStation Plus user base, using these insights to inform strategic decisions and improve performance measurability 

• Working across our Commercial, Engagement & Experience and CRO teams, you will be responsible for exploring our subscriptions data for novel insights, deconstructing complex datasets to help improve knowledge of our users 

• Creating recursive frameworks for long term monitoring of business initiatives, helping to identify opportunities for growth and reduce reporting cycle time 

• Working with our Engineering and Reporting teams to democratise insights into actionable outputs 

• Support efforts to optimise our business with experimentation and A/B testing 

• Partner closely with stakeholders within GSBO and other supporting teams to understand and deliver business insights using data 

• Collaborate with data teams across SIE to understand and utilise available tools and resources while contributing learnings and business area expertise to the organization  

What we are looking for 

• Bachelors Degree in a quantitative or computational field or equivalent

• 5+ years experience in analytics related roles 

• Proficient at translating ambiguous business requirements into meaningful analytical insights from start to finish  

• Skilled with SQL, Python and spreadsheet applications (Excel, Google sheets). 

• Knowledge of statistical concepts, subscription metrics and A/B testing 

• Keen attention to detail and ability to deliver high quality analytical work 

• Proven experience creating data visualizations and reports to surface actionable insights  

• Excellent communication skills (written and verbal) 

• Ability to work collaboratively with stakeholders across different teams and levels 

• Strong project management, multi-tasking and organizational skills 

• Ability to adapt quickly in a dynamic, changing, fast-paced business environment 

• Ability to work varying hours as well as a flexible approach to working hours to support global business operations 

Preferred Qualifications 

• Strong technical proficiencies with SQL, Python, Airflow 

• Skilled with visualization tools like Domo, Power BI, Tableau 

• Interest in the gaming industry 

• Knowledge of digital gaming landscape, key players, and competitive offerings 

• Prior experience with subscription businesses preferred 

As a Staff Data Scientist, you will lead innovation in experimentation and causal inference, helping shape the future of decision-making and product innovation at SIE. You will drive innovative research in experimentation methodologies while mentoring other team members. You’ll be responsible for elevating our experimentation strategy, fostering a culture of curiosity and rigor, and helping cross-functional teams deliver player-first experiences through strong evidence-based decisions.

What You’ll Be Doing:

• Drive innovation in experimentation research by shaping measurement frameworks and best practices, and by developing new methodologies that enhance the quality, speed, and scalability of experiments.
• Advance experimentation infrastructure and tooling, incorporating statistical and machine learning methods to refine analytical capabilities.
• Mentor and guide less-senior data scientists, building expertise in experimentation and causal inference while providing technical direction to ensure rigor and impact.
• Partner with product managers and business leaders to identify high-impact experimentation opportunities and align them with PlayStation’s strategic goals.
• Act as a thought leader in experimentation and causal inference, evangelizing best practices and fostering learning across teams.
• Contribute to research and prototyping of novel experimentation techniques that address complex real-world challenges, such as user behavior variability and data limitations.
• Champion a data-driven culture by establishing experimentation standards, ethical practices, and reproducibility
• Represent the team’s insights and innovations across the broader data science and product communities within PlayStation.
• Stay at the forefront of the field by monitoring emerging developments in experimentation, causal inference, and applied machine learning to continuously evolve capabilities.

What We’re Looking For:

• Master’s Degree or equivalent experience in Applied Math, Economics, Statistics, Computer Science, or related field. Ph.D. or equivalent experience preferred.
• Strong familiarity with the gaming industry and contemporary gaming experiences.
• 8+ years of experience in data science, including extensive hands-on work in experimentation, with at least 2+ years in a mentoring or technical leadership capacity.
• Proven track record of leading experimentation innovation and scaling frameworks within a dynamic business environment.
• Proficiency in SQL and statistical programming languages (e.g., R or Python), especially for causal inference, experimental analysis, and scalable modeling.
• Expertise in causal inference techniques and designing both randomized and quasi-experiments.
• Demonstrated ability to collaborate cross-functionally and influence data strategies that inform business and product decisions.
• Excellent communication and storytelling skills, especially in conveying complex concepts to non-technical stakeholders.
• Experience working with modern data engineering and visualization tools (e.g., Airflow, Git, Tableau, MicroStrategy).
• A strong sense of ownership and an inclusive leadership style that encourages collaboration and innovation.

Benefits:

• Discretionary bonus opportunity
• Hybrid Working (within Flexmodes)
• Private Medical Insurance
• Dental Scheme
• 25 days holiday per year
• On Site Gym
• Subsidised Café
• Free soft drinks
• On site bar
• Access to cycle garage and showers

Please note, Sony Interactive Entertainment conducts background checks at the offer stage for all new employees (which may include criminal background checks for some roles) and will need to process personal information to support these checks.

Please refer to our Candidate Privacy Notice for more information about what personal information we collect, how we use it, who we share it with, and your data protection rights. 

As a Data Science Manager, you will lead both people and innovation in experimentation and causal inference, helping shape the future of decision-making and product innovation at SIE. This is a hands-on leadership role blending technical depth with managerial responsibilities. You will drive cutting-edge research in experimentation methodologies while mentoring and guiding a team of data scientists. You’ll be responsible for elevating our experimentation strategy, fostering a culture of curiosity and rigor, and helping cross-functional teams deliver player-first experiences through strong evidence-based decisions.

What You’ll Be Doing:

• Lead a team of data scientists focused on experimentation and causal inference; provide technical direction, career development, and mentorship.
• Drive innovation in experimentation research by developing and overseeing new methodologies and frameworks that improve the quality, speed, and scalability of experiments.
• Guide the advancement of experimentation infrastructure and tooling, incorporating statistical and machine learning methods to refine analysis capabilities.
• Partner with product managers, game studios, and business leaders to identify high-impact experimentation opportunities and ensure alignment with PlayStation’s strategic goals.
• Act as a thought leader in experimentation and causal inference, evangelizing best practices and fostering learning across teams.
• Contribute directly to research and prototyping of novel experimentation techniques that address complex real-world constraints, such as user behavior variability and data limitations.
• Champion the growth of a data-driven culture by advocating for experimentation standards, ethical practices, and reproducibility.
• Represent the team’s insights, innovations, and impact across the broader data science and product communities within PlayStation.
• Stay abreast of emerging developments in experimentation, causal inference, and applied machine learning to continuously evolve our capabilities.

What We’re Looking For:

• Master’s Degree or equivalent experience in Applied Math, Economics, Statistics, Computer Science, or related field. Ph.D. or equivalent experience preferred.
• Strong familiarity with the gaming industry and contemporary gaming experiences.
• 6+ years of experience in data science, including hands-on work in experimentation, with at least 2+ years in a formal people management or technical leadership role.
• Proven track record of leading experimentation innovation and scaling frameworks within a dynamic business environment.
• Proficiency in SQL and statistical programming languages (e.g., R or Python), especially for causal inference, experimental analysis, and scalable modeling.
• Expertise in causal inference techniques and designing both randomized and quasi-experiments.
• Demonstrated ability to collaborate cross-functionally and influence data strategies that inform business and product decisions.
• Excellent communication and storytelling skills, especially in conveying complex concepts to non-technical stakeholders.
• Experience working with modern data engineering and visualization tools (e.g., Airflow, Git, Tableau, MicroStrategy).
• A strong sense of ownership and an inclusive leadership style that encourages collaboration and innovation.

Role Overview:

Our Data Science Analytics and Enablement (DSAE) team inspires PlayStation to make impactful, customer-centric decisions through seamless integration of data.

Currently there are over 100 people in the global DSAE team, including data science, data governance and analytics professionals. We work closely with engineering and product management teams to deliver data products, insight, predictive analytics, and data visualisation.

Data Scientists in the DSAE team apply sophisticated analytical methods and tools to drive value from the data through methods such as customer segmentation, predictive modelling, forecasting and other algorithms. Our work delivers an in-depth understanding of consumer behaviours and supports multi-channel targeting and personalisation.

We’re seeking a motivated and collaborative Data Scientist to join our CLV & Next Best Action / Personalisation team, which builds models that help PlayStation understand and influence the value, growth, and engagement of our global player base.

By connecting player behaviours to long-term outcomes, our work creates the intelligence that underpins Customer Lifetime Value (CLV) modelling and guides PlayStation’s Next Best Action and Personalisation strategies. You’ll help shape how PlayStation invests in, retains, and delights its players - and advance our use of modern machine learning at global scale.

This is an exciting opportunity to develop your skills in modern machine learning while contributing to high-impact projects that directly influence how PlayStation engages and retains its players.

What You’ll Be Doing:

• Build and validate models for use cases such as churn prediction, purchase propensity, CLV modelling, and personalised engagement.
• Work with sequential and behavioural data, applying machine learning approaches (e.g., gradient boosting, embeddings, sequence models) to capture player behaviour and growth.
• Partner with commercial, finance, and lifecycle teams to translate business needs into modelling hypotheses that inform engagement strategies.
• Collaborate with analytics and engineering colleagues to ensure models are efficient, robust, and scalable.
• Communicate findings clearly to both technical and non-technical partners, ensuring insights drive measurable outcomes.
• Grow your expertise in modern ML approaches (transformers, representation learning, sequence-to-sequence modelling) through high-visibility projects.

What we're looking for:

You’re curious, analytical, and eager to grow your skills in a commercial data science environment. You bring strong fundamentals in modelling and data manipulation, and you’re motivated by the challenge of applying these to impactful business problems.

• Strong analytical and critical thinking skills, with a passion for understanding customer behaviour and enabling data-driven growth.
• Practical experience with predictive modelling, such as churn, propensity, segmentation, or value modelling.
• Proficiency in Python and SQL, with fluency in common data science libraries.
• Ability to work with large datasets to uncover actionable insights that influence marketing, engagement, or retention strategies.
• Interest or early experience in deep learning approaches — such as transformers, embeddings, or sequence models — with motivation to learn and apply them.
• A collaborative attitude, with the ability to work in cross-functional teams.
• A strong academic background (typically with a Master’s in a technical or mathematical field) or equivalent professional experience.

Nice to Have:

• Experience with A/B testing or experimentation frameworks.
• Familiarity with workflow orchestration tools (e.g., Airflow), feature stores, or visualisation platforms (e.g., Tableau, Domo).
• Exposure to gaming, e-commerce, or subscription-based business models.
• Proven experience of analysing and modelling with “big data” technologies.
• A personal interest in gaming and entertainment.

Benefits:

• Discretionary bonus opportunity
• Hybrid Working (within Flexmodes)
• Private Medical Insurance
• Dental Scheme
• 25 days holiday per year
• On Site Gym
• Subsidised Café
• Free soft drinks
• On site bar
• Access to cycle garage and showers

Please note, Sony Interactive Entertainment conducts background checks at the offer stage for all new employees (which may include criminal background checks for some roles) and will need to process personal information to support these checks.

Please refer to our Candidate Privacy Notice for more information about what personal information we collect, how we use it, who we share it with, and your data protection rights. 

Job Title: Senior Python Engineer

Location: Remote (UK) with Occasional Travel - Applications from non-UK countries will be rejected
Division: Inizio Medical – Data & AI Team

About Inizio Medical

Inizio Medical is part of Inizio, the world’s leading healthcare and communications group. We are a trusted partner to pharmaceutical and healthcare clients, translating complex science into meaningful impact. Our division focuses on evidence-based communications and education for healthcare professionals and payers.

About the Role

We are seeking a senior, hands-on Python Engineer to join our growing Data & AI team. This role is ideal for an engineer who enjoys building robust backend systems, APIs, and services that power modern applications at scale.

You will work on the backend foundations of next-generation applications, including systems that incorporate generative AI and LLM-based capabilities. Prior experience with generative AI is not required—we are looking first and foremost for a strong Python engineer with excellent software engineering fundamentals who is interested in applying those skills in a fast-evolving domain.e

You’ll collaborate closely with software engineers, data scientists, and product teams to design, build, and operate production-grade systems used in real-world healthcare and life sciences environments.

Key Responsibilities

• Design, build, deploy, and maintain scalable Python services and APIs using modern backend and cloud-native patterns.
• Develop clean, testable, and well-structured Python code for data-intensive and workflow-driven applications.
• Build and integrate backend components that support AI-enabled features (e.g., orchestration layers, inference services, data pipelines).
• Integrate with third-party APIs and external services, ensuring reliability, performance, and cost efficiency.
• Own production concerns including logging, monitoring, error handling, and performance optimisation.
• Contribute to CI/CD pipelines, infrastructure-as-code, and containerised deployments.
• Collaborate with product, data, and engineering stakeholders to translate requirements into high-quality technical solutions.
• Write clear technical documentation for engineers and non-technical audiences.
• Contribute to a collaborative, pragmatic, and high-quality engineering culture.

About You

• Strong professional experience as a Python engineer, building and operating production systems (e.g., SaaS platforms, backend services, data-driven applications).
• Deep knowledge of Python fundamentals, including application architecture, performance considerations, testing, and maintainability.
• Experience building backend systems using frameworks such as FastAPI, Flask, Django, or similar.
• Solid understanding of cloud-based architectures (AWS and/or Azure), containers, and microservices.
• Experience working with CI/CD pipelines, version control, and modern development workflows.
• Strong debugging and problem-solving skills in distributed or data-heavy systems.
• Comfortable collaborating in cross-functional, Agile teams and taking ownership of delivered solutions.
• Curious, pragmatic, and motivated by building reliable systems that deliver real-world impact.

Nice to Have (Not Required)

• Exposure to data platforms or analytics workflows (e.g., Databricks, Airflow).
• Familiarity with LLMs, generative AI APIs, or AI-enabled application patterns.
• Experience with observability and monitoring tools (e.g., Prometheus, Grafana, Sentry).
• Contributions to open-source projects or shared internal libraries.
• Experience in regulated or high-compliance domains (e.g., healthcare, life sciences, finance).

What We Offer

• Competitive salary and benefits package, including private medical insurance and company pension.
• Flexible, remote-first working environment with occasional travel.
• The opportunity to work on meaningful, high-impact products within a global healthcare organisation.
• A friendly, low-ego culture that values engineering quality, learning, and collaboration.

Ready to Apply?

If you’re a strong Python engineer who enjoys building high-quality systems and is interested in applying your skills to emerging technologies in healthcare, we’d love to hear from you.

Winton leverages quantitative analysis and cutting-edge technology to identify and capitalize on opportunities across global financial markets. We foster a collaborative and intellectually stimulating environment, bringing together individuals with Mathematics, Physics and Computer Science backgrounds who are passionate about applying rigorous scientific methods to financial challenges. As a fundamentally data-driven business, our success is heavily linked to the acquisition, processing, and analysis of vast datasets. High-quality, well-managed data forms the critical foundation for our quantitative research, strategy development, and automated trading systems.
 
As a Data Engineer within our Quantitative Platform team, you will play a pivotal role in building and maintaining the data infrastructure that fuels our research and trading strategies. You will be responsible for the end-to-end lifecycle of diverse datasets – including market, fundamental, and alternative sources – ensuring their timely acquisition, rigorous cleaning and validation, efficient storage, and reliable delivery through robust data pipelines. Working closely with quantitative researchers and technologists, you will tackle complex challenges in data quality, normalization, and accessibility, ultimately providing the high-fidelity, readily available data essential for developing and executing sophisticated investment models in a fast-paced environment.
 

Your responsibilities will include:

• Evaluating, onboarding, and integrating complex data products from diverse vendors, serving as a key technical liaison to ensure data feeds meet our stringent requirements for research and live trading.
• Designing, implementing, and optimizing robust, production-grade data pipelines to transform raw vendor data into analysis-ready datasets, adhering to software engineering best practices and ensuring seamless consumption by our automated trading systems.
• Engineering and maintaining sophisticated automated validation frameworks to guarantee the accuracy, timeliness, and integrity of all datasets, directly upholding the quality standards essential for the efficacy of our quantitative strategies.
• Providing expert operational support for our data pipelines, rapidly diagnosing and resolving critical issues to ensure the uninterrupted flow of high-availability data powering our daily trading activities.
• Participating actively in team rotations, including on-call schedules, to provide essential coverage and maintain the resilience of our data systems outside of standard business hours.

What we are looking for:

• 1+ years’ experience building ETL/ELT pipelines using Python
• Familiarity with various technologies such as S3, Kafka, Airflow, Iceberg. 
• A commitment to engineering excellence and pragmatic technology solutions.
• A desire to work in an operational role at the heart of a dynamic data-centric enterprise.
• Excellent communication and collaboration skills, and the ability to work in a team.

What would be advantageous:

• Strong understanding of financial markets.
• Proficiency working with large financial datasets from various vendors.
• Experience working with hierarchical reference data models.
• Proven expertise in handling high-throughput, real-time market data streams
• Familiarity with distributed computing frameworks such as Apache Spark
• Operational experience supporting real time system

As a Customer Data Scientist at Datadog, you will partner directly with customers to help them design, execute, and scale high-impact experimentation programs. This role is ideal for someone with deep experience in applied experimentation who thrives in customer-facing environments and is motivated by driving measurable business outcomes. You will guide customers through experiment design, metric development, and data validation, helping them build trustworthy, decision-driven experimentation practices. Working cross-functionally with Sales, Product, and Engineering, you’ll play a key role in both customer success and product evolution.

At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.

What You’ll Do:

• Serve as a technical and analytical partner to customers across pre-sales and post-sales engagements, demonstrating the value of Datadog’s experimentation capabilities
• Support proofs of concept by helping customers define metrics, validate data pipelines, and design statistically sound experiments
• Educate customers on best practices in experiment design and interpretation across product, marketing, pricing, and AI-driven use cases
• Enable long-term customer success by building strong experimentation practices and analytical rigor within their teams
• Collaborate with Product and Engineering to translate customer feedback into product improvements and roadmap input
• Partner with Sales and Solutions teams to navigate complex customer needs, address objections, and build trusted relationships

Who You Are:

• 4+ years of experience in data science with a strong foundation in statistics, causal inference, and experimental design in applied business or product contexts
• Hands-on experience running A/B tests and applying advanced experimentation methods such as holdouts, bandits, synthetic controls, or geo-based testing
• Fluent in SQL and comfortable working with data warehouses and pipelines to diagnose and resolve data quality issues
• Experienced in customer-facing, consulting, or pre-sales environments, with the ability to communicate complex ideas clearly to diverse audiences
• Skilled at translating analytical insights into business impact, influencing decisions across technical and non-technical stakeholders
• Experience with experimentation platforms, analytics tooling, or modern data stack technologies (e.g., dbt, Airflow) is a plus

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply.

 
Benefits and Growth:

• Generous and competitive benefits package
• New hire stock equity (RSUs) and employee stock purchase plan
• Continuous career development and pathing opportunities
• Employee-focused best in class onboarding
• Internal mentor and cross-departmental buddy program
• Friendly and inclusive workplace culture
  
  

Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

#LI-Hybrid

Location: Remote with expected travel into Paddington 2 days per week.

How You'll Make an Impact:

As a Machine Learning Ops Engineer, you will play a key role in building and implementing features that empower lodging customers to make data-driven pricing decisions. Some of these features will use simple heuristic data, while others will leverage advanced machine learning techniques to optimize revenue strategies.

You’ll work closely with product and engineering teams to identify opportunities for improvement, develop innovative solutions, and drive revenue growth for the hotels that rely on our platform. Your impact will be focused on ensuring the reliability, scalability, and high quality of our ML systems from development to production. You’ll be instrumental in establishing robust MLOps practices and rigorous testing processes across the entire ML lifecycle. From structuring data pipelines to implementing and validating ML models, you’ll own the end-to-end development of our revenue management application—ensuring hotels have the reliable, accurate insights they need to maximize their success.

Our Machine Learning Team:

Our machine learning team is energized by the unique challenge of revolutionizing guest experiences through AI-driven insights, transforming traditional hospitality with cutting-edge predictive algorithms. 

We thrive on collaborative innovation, where data scientists, engineers, and product experts seamlessly blend their expertise to prototype bold ideas and directly impact operational efficiency. 

People who are passionate about continuous learning, unafraid to challenge conventions, and excited by the intersection of hospitality and deep technical prowess will find their home among our forward-thinking team.

What You Bring to the Team:

1. Develop and implement end-to-end machine learning features that enable customers to optimize their revenue strategies, with a strong emphasis on production readiness and system reliability.
2. Establish and maintain robust MLOps practices including CI/CD for model training, testing, deployment, and monitoring.
3. Design, build, and maintain highly reliable and well-tested data and ML pipelines to extract, transform, and structure large datasets for ML applications.
4. Expertise in using Apache Airflow (or similar orchestration tools like Prefect/Dagster) to define, schedule, and monitor complex data and ML workflows (DAGs).
5. Implement comprehensive software quality and testing processes for ML systems, including unit, integration, and end-to-end testing for both code and data/model performance.
6. Design, train, and rigorously test machine learning models where needed to improve pricing optimization, focusing on statistical validation and production stability.
7. Implement model performance monitoring (e.g., drift detection, data quality checks) to ensure deployed models maintain accuracy and relevance over time.
8. Collaborate cross-functionally with product, engineering, and data science teams to define SLIs/SLOs for ML services and improve system performance, stability, and usability.
9. Conduct structured A/B testing and experimentation to validate model effectiveness and continuously improve performance, documenting results and sharing technical insights.

What Sets You Up for Success:

1. Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.
2. 3+ years of experience in a data engineering or machine learning role, with demonstrated success in MLOps and deploying models to production.
3. Proven expertise in designing and implementing ML testing strategies (e.g., data validation, model correctness, performance testing).
4. Expertise in deploying ML models at scale on AWS, with experience using MLFlow or similar platforms.
5. Strong Python programming skills and adherence to software engineering best practices (e.g., clean code, version control, code reviews).
6. Expert-level SQL skills and experience working with large datasets for analysis and modeling.
7. Strong problem-solving skills with the ability to apply creative, data-driven solutions to complex business challenges.
8. Excellent communication and collaboration skills, with experience working cross-functionally with product and engineering teams.

Bonus Skills to Stand Out (Optional):

1. Experience with CI/CD tooling (e.g., GitHub Actions, Jenkins) specifically for ML pipelines and Airflow DAG deployment.
2. Experience with data quality monitoring tools and frameworks.
3. Master’s or PhD in Computer Science, Data Science, or a related field. Relevant certifications (AWS, MLFlow, or other data science/ML certifications).

#LI-REMOTE #LI-AM1

Location: Remote with expected travel into Paddington 2 days per week.

How You'll Make an Impact:

As a Machine Learning Ops Engineer, you will play a key role in building and implementing features that empower lodging customers to make data-driven pricing decisions. Some of these features will use simple heuristic data, while others will leverage advanced machine learning techniques to optimize revenue strategies.

You’ll work closely with product and engineering teams to identify opportunities for improvement, develop innovative solutions, and drive revenue growth for the hotels that rely on our platform. Your impact will be focused on ensuring the reliability, scalability, and high quality of our ML systems from development to production. You’ll be instrumental in establishing robust MLOps practices and rigorous testing processes across the entire ML lifecycle. From structuring data pipelines to implementing and validating ML models, you’ll own the end-to-end development of our revenue management application—ensuring hotels have the reliable, accurate insights they need to maximize their success.

Our Machine Learning Team:

Our machine learning team is energized by the unique challenge of revolutionizing guest experiences through AI-driven insights, transforming traditional hospitality with cutting-edge predictive algorithms. 

We thrive on collaborative innovation, where data scientists, engineers, and product experts seamlessly blend their expertise to prototype bold ideas and directly impact operational efficiency. 

People who are passionate about continuous learning, unafraid to challenge conventions, and excited by the intersection of hospitality and deep technical prowess will find their home among our forward-thinking team.

What You Bring to the Team:

1. Develop and implement end-to-end machine learning features that enable customers to optimize their revenue strategies, with a strong emphasis on production readiness and system reliability.
2. Establish and maintain robust MLOps practices including CI/CD for model training, testing, deployment, and monitoring.
3. Design, build, and maintain highly reliable and well-tested data and ML pipelines to extract, transform, and structure large datasets for ML applications.
4. Expertise in using Apache Airflow (or similar orchestration tools like Prefect/Dagster) to define, schedule, and monitor complex data and ML workflows (DAGs).
5. Implement comprehensive software quality and testing processes for ML systems, including unit, integration, and end-to-end testing for both code and data/model performance.
6. Design, train, and rigorously test machine learning models where needed to improve pricing optimization, focusing on statistical validation and production stability.
7. Implement model performance monitoring (e.g., drift detection, data quality checks) to ensure deployed models maintain accuracy and relevance over time.
8. Collaborate cross-functionally with product, engineering, and data science teams to define SLIs/SLOs for ML services and improve system performance, stability, and usability.
9. Conduct structured A/B testing and experimentation to validate model effectiveness and continuously improve performance, documenting results and sharing technical insights.

What Sets You Up for Success:

1. Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.
2. 3+ years of experience in a data engineering or machine learning role, with demonstrated success in MLOps and deploying models to production.
3. Proven expertise in designing and implementing ML testing strategies (e.g., data validation, model correctness, performance testing).
4. Expertise in deploying ML models at scale on AWS, with experience using MLFlow or similar platforms.
5. Strong Python programming skills and adherence to software engineering best practices (e.g., clean code, version control, code reviews).
6. Expert-level SQL skills and experience working with large datasets for analysis and modeling.
7. Strong problem-solving skills with the ability to apply creative, data-driven solutions to complex business challenges.
8. Excellent communication and collaboration skills, with experience working cross-functionally with product and engineering teams.

Bonus Skills to Stand Out (Optional):

1. Experience with CI/CD tooling (e.g., GitHub Actions, Jenkins) specifically for ML pipelines and Airflow DAG deployment.
2. Experience with data quality monitoring tools and frameworks.
3. Master’s or PhD in Computer Science, Data Science, or a related field. Relevant certifications (AWS, MLFlow, or other data science/ML certifications).

#LI-REMOTE #LI-AM1

Location: Remote with expected travel into Paddington 2 days per week.

How You'll Make an Impact:

As a Machine Learning Ops Engineer, you will play a key role in building and implementing features that empower lodging customers to make data-driven pricing decisions. Some of these features will use simple heuristic data, while others will leverage advanced machine learning techniques to optimize revenue strategies.

You’ll work closely with product and engineering teams to identify opportunities for improvement, develop innovative solutions, and drive revenue growth for the hotels that rely on our platform. Your impact will be focused on ensuring the reliability, scalability, and high quality of our ML systems from development to production. You’ll be instrumental in establishing robust MLOps practices and rigorous testing processes across the entire ML lifecycle. From structuring data pipelines to implementing and validating ML models, you’ll own the end-to-end development of our revenue management application—ensuring hotels have the reliable, accurate insights they need to maximize their success.

Our Machine Learning Team:

Our machine learning team is energized by the unique challenge of revolutionizing guest experiences through AI-driven insights, transforming traditional hospitality with cutting-edge predictive algorithms. 

We thrive on collaborative innovation, where data scientists, engineers, and product experts seamlessly blend their expertise to prototype bold ideas and directly impact operational efficiency. 

People who are passionate about continuous learning, unafraid to challenge conventions, and excited by the intersection of hospitality and deep technical prowess will find their home among our forward-thinking team.

What You Bring to the Team:

1. Develop and implement end-to-end machine learning features that enable customers to optimize their revenue strategies, with a strong emphasis on production readiness and system reliability.
2. Establish and maintain robust MLOps practices including CI/CD for model training, testing, deployment, and monitoring.
3. Design, build, and maintain highly reliable and well-tested data and ML pipelines to extract, transform, and structure large datasets for ML applications.
4. Expertise in using Apache Airflow (or similar orchestration tools like Prefect/Dagster) to define, schedule, and monitor complex data and ML workflows (DAGs).
5. Implement comprehensive software quality and testing processes for ML systems, including unit, integration, and end-to-end testing for both code and data/model performance.
6. Design, train, and rigorously test machine learning models where needed to improve pricing optimization, focusing on statistical validation and production stability.
7. Implement model performance monitoring (e.g., drift detection, data quality checks) to ensure deployed models maintain accuracy and relevance over time.
8. Collaborate cross-functionally with product, engineering, and data science teams to define SLIs/SLOs for ML services and improve system performance, stability, and usability.
9. Conduct structured A/B testing and experimentation to validate model effectiveness and continuously improve performance, documenting results and sharing technical insights.

What Sets You Up for Success:

1. Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.
2. 3+ years of experience in a data engineering or machine learning role, with demonstrated success in MLOps and deploying models to production.
3. Proven expertise in designing and implementing ML testing strategies (e.g., data validation, model correctness, performance testing).
4. Expertise in deploying ML models at scale on AWS, with experience using MLFlow or similar platforms.
5. Strong Python programming skills and adherence to software engineering best practices (e.g., clean code, version control, code reviews).
6. Expert-level SQL skills and experience working with large datasets for analysis and modeling.
7. Strong problem-solving skills with the ability to apply creative, data-driven solutions to complex business challenges.
8. Excellent communication and collaboration skills, with experience working cross-functionally with product and engineering teams.

Bonus Skills to Stand Out (Optional):

1. Experience with CI/CD tooling (e.g., GitHub Actions, Jenkins) specifically for ML pipelines and Airflow DAG deployment.
2. Experience with data quality monitoring tools and frameworks.
3. Master’s or PhD in Computer Science, Data Science, or a related field. Relevant certifications (AWS, MLFlow, or other data science/ML certifications).

#LI-REMOTE #LI-AM1

Location: Remote with expected travel into Paddington 2 days per week.

How You'll Make an Impact:

As a Machine Learning Ops Engineer, you will play a key role in building and implementing features that empower lodging customers to make data-driven pricing decisions. Some of these features will use simple heuristic data, while others will leverage advanced machine learning techniques to optimize revenue strategies.

You’ll work closely with product and engineering teams to identify opportunities for improvement, develop innovative solutions, and drive revenue growth for the hotels that rely on our platform. Your impact will be focused on ensuring the reliability, scalability, and high quality of our ML systems from development to production. You’ll be instrumental in establishing robust MLOps practices and rigorous testing processes across the entire ML lifecycle. From structuring data pipelines to implementing and validating ML models, you’ll own the end-to-end development of our revenue management application—ensuring hotels have the reliable, accurate insights they need to maximize their success.

Our Machine Learning Team:

Our machine learning team is energized by the unique challenge of revolutionizing guest experiences through AI-driven insights, transforming traditional hospitality with cutting-edge predictive algorithms. 

We thrive on collaborative innovation, where data scientists, engineers, and product experts seamlessly blend their expertise to prototype bold ideas and directly impact operational efficiency. 

People who are passionate about continuous learning, unafraid to challenge conventions, and excited by the intersection of hospitality and deep technical prowess will find their home among our forward-thinking team.

What You Bring to the Team:

1. Develop and implement end-to-end machine learning features that enable customers to optimize their revenue strategies, with a strong emphasis on production readiness and system reliability.
2. Establish and maintain robust MLOps practices including CI/CD for model training, testing, deployment, and monitoring.
3. Design, build, and maintain highly reliable and well-tested data and ML pipelines to extract, transform, and structure large datasets for ML applications.
4. Expertise in using Apache Airflow (or similar orchestration tools like Prefect/Dagster) to define, schedule, and monitor complex data and ML workflows (DAGs).
5. Implement comprehensive software quality and testing processes for ML systems, including unit, integration, and end-to-end testing for both code and data/model performance.
6. Design, train, and rigorously test machine learning models where needed to improve pricing optimization, focusing on statistical validation and production stability.
7. Implement model performance monitoring (e.g., drift detection, data quality checks) to ensure deployed models maintain accuracy and relevance over time.
8. Collaborate cross-functionally with product, engineering, and data science teams to define SLIs/SLOs for ML services and improve system performance, stability, and usability.
9. Conduct structured A/B testing and experimentation to validate model effectiveness and continuously improve performance, documenting results and sharing technical insights.

What Sets You Up for Success:

1. Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.
2. 3+ years of experience in a data engineering or machine learning role, with demonstrated success in MLOps and deploying models to production.
3. Proven expertise in designing and implementing ML testing strategies (e.g., data validation, model correctness, performance testing).
4. Expertise in deploying ML models at scale on AWS, with experience using MLFlow or similar platforms.
5. Strong Python programming skills and adherence to software engineering best practices (e.g., clean code, version control, code reviews).
6. Expert-level SQL skills and experience working with large datasets for analysis and modeling.
7. Strong problem-solving skills with the ability to apply creative, data-driven solutions to complex business challenges.
8. Excellent communication and collaboration skills, with experience working cross-functionally with product and engineering teams.

Bonus Skills to Stand Out (Optional):

1. Experience with CI/CD tooling (e.g., GitHub Actions, Jenkins) specifically for ML pipelines and Airflow DAG deployment.
2. Experience with data quality monitoring tools and frameworks.
3. Master’s or PhD in Computer Science, Data Science, or a related field. Relevant certifications (AWS, MLFlow, or other data science/ML certifications).

#LI-REMOTE #LI-AM1

Location: Remote with expected travel into Paddington 2 days per week.

How You'll Make an Impact:

As a Machine Learning Ops Engineer, you will play a key role in building and implementing features that empower lodging customers to make data-driven pricing decisions. Some of these features will use simple heuristic data, while others will leverage advanced machine learning techniques to optimize revenue strategies.

You’ll work closely with product and engineering teams to identify opportunities for improvement, develop innovative solutions, and drive revenue growth for the hotels that rely on our platform. Your impact will be focused on ensuring the reliability, scalability, and high quality of our ML systems from development to production. You’ll be instrumental in establishing robust MLOps practices and rigorous testing processes across the entire ML lifecycle. From structuring data pipelines to implementing and validating ML models, you’ll own the end-to-end development of our revenue management application—ensuring hotels have the reliable, accurate insights they need to maximize their success.

Our Machine Learning Team:

Our machine learning team is energized by the unique challenge of revolutionizing guest experiences through AI-driven insights, transforming traditional hospitality with cutting-edge predictive algorithms. 

We thrive on collaborative innovation, where data scientists, engineers, and product experts seamlessly blend their expertise to prototype bold ideas and directly impact operational efficiency. 

People who are passionate about continuous learning, unafraid to challenge conventions, and excited by the intersection of hospitality and deep technical prowess will find their home among our forward-thinking team.

What You Bring to the Team:

1. Develop and implement end-to-end machine learning features that enable customers to optimize their revenue strategies, with a strong emphasis on production readiness and system reliability.
2. Establish and maintain robust MLOps practices including CI/CD for model training, testing, deployment, and monitoring.
3. Design, build, and maintain highly reliable and well-tested data and ML pipelines to extract, transform, and structure large datasets for ML applications.
4. Expertise in using Apache Airflow (or similar orchestration tools like Prefect/Dagster) to define, schedule, and monitor complex data and ML workflows (DAGs).
5. Implement comprehensive software quality and testing processes for ML systems, including unit, integration, and end-to-end testing for both code and data/model performance.
6. Design, train, and rigorously test machine learning models where needed to improve pricing optimization, focusing on statistical validation and production stability.
7. Implement model performance monitoring (e.g., drift detection, data quality checks) to ensure deployed models maintain accuracy and relevance over time.
8. Collaborate cross-functionally with product, engineering, and data science teams to define SLIs/SLOs for ML services and improve system performance, stability, and usability.
9. Conduct structured A/B testing and experimentation to validate model effectiveness and continuously improve performance, documenting results and sharing technical insights.

What Sets You Up for Success:

1. Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.
2. 3+ years of experience in a data engineering or machine learning role, with demonstrated success in MLOps and deploying models to production.
3. Proven expertise in designing and implementing ML testing strategies (e.g., data validation, model correctness, performance testing).
4. Expertise in deploying ML models at scale on AWS, with experience using MLFlow or similar platforms.
5. Strong Python programming skills and adherence to software engineering best practices (e.g., clean code, version control, code reviews).
6. Expert-level SQL skills and experience working with large datasets for analysis and modeling.
7. Strong problem-solving skills with the ability to apply creative, data-driven solutions to complex business challenges.
8. Excellent communication and collaboration skills, with experience working cross-functionally with product and engineering teams.

Bonus Skills to Stand Out (Optional):

1. Experience with CI/CD tooling (e.g., GitHub Actions, Jenkins) specifically for ML pipelines and Airflow DAG deployment.
2. Experience with data quality monitoring tools and frameworks.
3. Master’s or PhD in Computer Science, Data Science, or a related field. Relevant certifications (AWS, MLFlow, or other data science/ML certifications).

#LI-REMOTE #LI-AM1

Location: Remote with expected travel into Paddington 2 days per week.

How You'll Make an Impact:

As a Machine Learning Ops Engineer, you will play a key role in building and implementing features that empower lodging customers to make data-driven pricing decisions. Some of these features will use simple heuristic data, while others will leverage advanced machine learning techniques to optimize revenue strategies.

You’ll work closely with product and engineering teams to identify opportunities for improvement, develop innovative solutions, and drive revenue growth for the hotels that rely on our platform. Your impact will be focused on ensuring the reliability, scalability, and high quality of our ML systems from development to production. You’ll be instrumental in establishing robust MLOps practices and rigorous testing processes across the entire ML lifecycle. From structuring data pipelines to implementing and validating ML models, you’ll own the end-to-end development of our revenue management application—ensuring hotels have the reliable, accurate insights they need to maximize their success.

Our Machine Learning Team:

Our machine learning team is energized by the unique challenge of revolutionizing guest experiences through AI-driven insights, transforming traditional hospitality with cutting-edge predictive algorithms. 

We thrive on collaborative innovation, where data scientists, engineers, and product experts seamlessly blend their expertise to prototype bold ideas and directly impact operational efficiency. 

People who are passionate about continuous learning, unafraid to challenge conventions, and excited by the intersection of hospitality and deep technical prowess will find their home among our forward-thinking team.

What You Bring to the Team:

1. Develop and implement end-to-end machine learning features that enable customers to optimize their revenue strategies, with a strong emphasis on production readiness and system reliability.
2. Establish and maintain robust MLOps practices including CI/CD for model training, testing, deployment, and monitoring.
3. Design, build, and maintain highly reliable and well-tested data and ML pipelines to extract, transform, and structure large datasets for ML applications.
4. Expertise in using Apache Airflow (or similar orchestration tools like Prefect/Dagster) to define, schedule, and monitor complex data and ML workflows (DAGs).
5. Implement comprehensive software quality and testing processes for ML systems, including unit, integration, and end-to-end testing for both code and data/model performance.
6. Design, train, and rigorously test machine learning models where needed to improve pricing optimization, focusing on statistical validation and production stability.
7. Implement model performance monitoring (e.g., drift detection, data quality checks) to ensure deployed models maintain accuracy and relevance over time.
8. Collaborate cross-functionally with product, engineering, and data science teams to define SLIs/SLOs for ML services and improve system performance, stability, and usability.
9. Conduct structured A/B testing and experimentation to validate model effectiveness and continuously improve performance, documenting results and sharing technical insights.

What Sets You Up for Success:

1. Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.
2. 3+ years of experience in a data engineering or machine learning role, with demonstrated success in MLOps and deploying models to production.
3. Proven expertise in designing and implementing ML testing strategies (e.g., data validation, model correctness, performance testing).
4. Expertise in deploying ML models at scale on AWS, with experience using MLFlow or similar platforms.
5. Strong Python programming skills and adherence to software engineering best practices (e.g., clean code, version control, code reviews).
6. Expert-level SQL skills and experience working with large datasets for analysis and modeling.
7. Strong problem-solving skills with the ability to apply creative, data-driven solutions to complex business challenges.
8. Excellent communication and collaboration skills, with experience working cross-functionally with product and engineering teams.

Bonus Skills to Stand Out (Optional):

1. Experience with CI/CD tooling (e.g., GitHub Actions, Jenkins) specifically for ML pipelines and Airflow DAG deployment.
2. Experience with data quality monitoring tools and frameworks.
3. Master’s or PhD in Computer Science, Data Science, or a related field. Relevant certifications (AWS, MLFlow, or other data science/ML certifications).

#LI-REMOTE #LI-AM1

Location: Remote with expected travel into Paddington 2 days per week.

How You'll Make an Impact:

As a Machine Learning Ops Engineer, you will play a key role in building and implementing features that empower lodging customers to make data-driven pricing decisions. Some of these features will use simple heuristic data, while others will leverage advanced machine learning techniques to optimize revenue strategies.

You’ll work closely with product and engineering teams to identify opportunities for improvement, develop innovative solutions, and drive revenue growth for the hotels that rely on our platform. Your impact will be focused on ensuring the reliability, scalability, and high quality of our ML systems from development to production. You’ll be instrumental in establishing robust MLOps practices and rigorous testing processes across the entire ML lifecycle. From structuring data pipelines to implementing and validating ML models, you’ll own the end-to-end development of our revenue management application—ensuring hotels have the reliable, accurate insights they need to maximize their success.

Our Machine Learning Team:

Our machine learning team is energized by the unique challenge of revolutionizing guest experiences through AI-driven insights, transforming traditional hospitality with cutting-edge predictive algorithms. 

We thrive on collaborative innovation, where data scientists, engineers, and product experts seamlessly blend their expertise to prototype bold ideas and directly impact operational efficiency. 

People who are passionate about continuous learning, unafraid to challenge conventions, and excited by the intersection of hospitality and deep technical prowess will find their home among our forward-thinking team.

What You Bring to the Team:

1. Develop and implement end-to-end machine learning features that enable customers to optimize their revenue strategies, with a strong emphasis on production readiness and system reliability.
2. Establish and maintain robust MLOps practices, including CI/CD for model training, testing, deployment, and monitoring.
3. Design, build, and maintain highly reliable and well-tested data and ML pipelines to extract, transform, and structure large datasets for ML applications.
4. Expertise in using Apache Airflow (or similar orchestration tools like Prefect/Dagster) to define, schedule, and monitor complex data and ML workflows (DAGs).
5. Implement comprehensive software quality and testing processes for ML systems, including unit, integration, and end-to-end testing for both code and data/model performance.
6. Design, train, and rigorously test machine learning models where needed to improve pricing optimization, focusing on statistical validation and production stability.
7. Implement model performance monitoring (e.g., drift detection, data quality checks) to ensure deployed models maintain accuracy and relevance over time.
8. Collaborate cross-functionally with product, engineering, and data science teams to define SLIs/SLOs for ML services and improve system performance, stability, and usability.
9. Conduct structured A/B testing and experimentation to validate model effectiveness and continuously improve performance, documenting results and sharing technical insights.

What Sets You Up for Success:

1. Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.
2. 3+ years of experience in a data engineering or machine learning role, with demonstrated success in MLOps and deploying models to production.
3. Proven expertise in designing and implementing ML testing strategies (e.g., data validation, model correctness, performance testing).
4. Expertise in deploying ML models at scale on AWS, with experience using MLFlow or similar platforms.
5. Strong Python programming skills and adherence to software engineering best practices (e.g., clean code, version control, code reviews).
6. Expert-level SQL skills and experience working with large datasets for analysis and modeling.
7. Strong problem-solving skills with the ability to apply creative, data-driven solutions to complex business challenges.
8. Excellent communication and collaboration skills, with experience working cross-functionally with product and engineering teams.

Bonus Skills to Stand Out (Optional):

1. Experience with CI/CD tooling (e.g., GitHub Actions, Jenkins) specifically for ML pipelines and Airflow DAG deployment.
2. Experience with data quality monitoring tools and frameworks.
3. Master’s or PhD in Computer Science, Data Science, or a related field. Relevant certifications (AWS, MLFlow, or other data science/ML certifications).

#LI-AM1

Location: Remote with expected travel into Paddington 2 days per week.

How You'll Make an Impact:

As a Machine Learning Ops Engineer, you will play a key role in building and implementing features that empower lodging customers to make data-driven pricing decisions. Some of these features will use simple heuristic data, while others will leverage advanced machine learning techniques to optimize revenue strategies.

You’ll work closely with product and engineering teams to identify opportunities for improvement, develop innovative solutions, and drive revenue growth for the hotels that rely on our platform. Your impact will be focused on ensuring the reliability, scalability, and high quality of our ML systems from development to production. You’ll be instrumental in establishing robust MLOps practices and rigorous testing processes across the entire ML lifecycle. From structuring data pipelines to implementing and validating ML models, you’ll own the end-to-end development of our revenue management application—ensuring hotels have the reliable, accurate insights they need to maximize their success.

Our Machine Learning Team:

Our machine learning team is energized by the unique challenge of revolutionizing guest experiences through AI-driven insights, transforming traditional hospitality with cutting-edge predictive algorithms. 

We thrive on collaborative innovation, where data scientists, engineers, and product experts seamlessly blend their expertise to prototype bold ideas and directly impact operational efficiency. 

People who are passionate about continuous learning, unafraid to challenge conventions, and excited by the intersection of hospitality and deep technical prowess will find their home among our forward-thinking team.

What You Bring to the Team:

1. Develop and implement end-to-end machine learning features that enable customers to optimize their revenue strategies, with a strong emphasis on production readiness and system reliability.
2. Establish and maintain robust MLOps practices, including CI/CD for model training, testing, deployment, and monitoring.
3. Design, build, and maintain highly reliable and well-tested data and ML pipelines to extract, transform, and structure large datasets for ML applications.
4. Expertise in using Apache Airflow (or similar orchestration tools like Prefect/Dagster) to define, schedule, and monitor complex data and ML workflows (DAGs).
5. Implement comprehensive software quality and testing processes for ML systems, including unit, integration, and end-to-end testing for both code and data/model performance.
6. Design, train, and rigorously test machine learning models where needed to improve pricing optimization, focusing on statistical validation and production stability.
7. Implement model performance monitoring (e.g., drift detection, data quality checks) to ensure deployed models maintain accuracy and relevance over time.
8. Collaborate cross-functionally with product, engineering, and data science teams to define SLIs/SLOs for ML services and improve system performance, stability, and usability.
9. Conduct structured A/B testing and experimentation to validate model effectiveness and continuously improve performance, documenting results and sharing technical insights.

What Sets You Up for Success:

1. Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.
2. 3+ years of experience in a data engineering or machine learning role, with demonstrated success in MLOps and deploying models to production.
3. Proven expertise in designing and implementing ML testing strategies (e.g., data validation, model correctness, performance testing).
4. Expertise in deploying ML models at scale on AWS, with experience using MLFlow or similar platforms.
5. Strong Python programming skills and adherence to software engineering best practices (e.g., clean code, version control, code reviews).
6. Expert-level SQL skills and experience working with large datasets for analysis and modeling.
7. Strong problem-solving skills with the ability to apply creative, data-driven solutions to complex business challenges.
8. Excellent communication and collaboration skills, with experience working cross-functionally with product and engineering teams.

Bonus Skills to Stand Out (Optional):

1. Experience with CI/CD tooling (e.g., GitHub Actions, Jenkins) specifically for ML pipelines and Airflow DAG deployment.
2. Experience with data quality monitoring tools and frameworks.
3. Master’s or PhD in Computer Science, Data Science, or a related field. Relevant certifications (AWS, MLFlow, or other data science/ML certifications).

The role

We’re looking for a Software Engineer with strong C++ expertise to join the team building and operating Nebius Data Platform — a distributed storage and a processing platform that acts as the company’s “source of truth” and the backbone of many internal (and some external) products.

Nebius Data Platform is a single multi-tenant ecosystem based on YTsaurus — instead of running separate HDFS/Kafka/HBase-style systems, we provide storage, compute, and analytics capabilities inside one platform.

Built on top of the open-source YTsaurus ecosystem, we run and extend our own Nebius distribution and develop significant in-house functionality (core and platform-level). We can design, implement, and roll out features end-to-end on our clusters without waiting for upstream approvals and contribute upstream when it makes sense.

At scale today, this includes~500 servers, ~20k CPU cores and ~10 PB of compressed data in our largest production cluster, supporting workloads ranging from business-critical pipelines and financial transactions to large-scale ML/LLM training datasets and compute.

What’s inside the platform

You’ll work on a system that includes (and ties together):

• Distributed Storage (Cypress): transactional semantics, tiered storage, erasure coding, replication, and strong reliability expectations.
• Compute & ETL: a cluster-wide job scheduler (tens of thousands of cores), MapReduce, YQL for SQL-like data processing, and SPYT (Spark over YTsaurus) for modern data engineering.
• Interactive analytics (CHYT): ClickHouse® instances spun up directly on compute nodes for fast SQL over data in-place.
• Dynamic Tables: low-latency NoSQL KV with distributed ACID transactions for OLTP-style workloads and feature stores.
• Orchestracto: workflow orchestration deeply integrated with the platform (Airflow-like, but platform-native).

What you’ll do

We’re looking for engineers who combine strong systems skills with product sense: understanding who uses the platform, why certain capabilities matter, and making pragmatic trade-offs to maximize impact. On our team, engineering work is expected to be connected to real users and outcomes — you’ll regularly align with internal stakeholders, clarify requirements, and help drive prioritization.

In this role, you will:

• Design and implement new functionality in YTsaurus core (C++) with production reliability in mind.
• Build and evolve platform-level capabilities: platform architecture and operating model—multi-cluster growth, shared primitives, and a consistent experience that scales with new teams and use cases.
• Improve end-to-end platform experience for internal (and external-facing) users: APIs, guardrails, debugging workflows, and automation.
• Own production quality: incident response / on-call rotation, root cause analysis, and turning learnings into durable fixes.

Example projects

• Roll out sharded YTsaurus masters (incl. Kubernetes operator support) and build automatic balancing of metadata across master cells (consensus groups) to remove control-plane bottlenecks and unlock 10–100x cluster growth.
• Make CHYT interactive SQL faster and more predictable at high load via performance work like data-skipping / min-max-style indexes and improved execution introspection.
• Turn Orchestracto into a platform product by defining the building blocks, developer experience, and governance for how teams create and share workflows.
• Scale and harden Parquet-on-S3 for native YTsaurus workloads by tackling replication/movement, consistent lifecycle semantics, and master-server metadata optimizations for performance and reliability.
• Design and ship complete, trustworthy audit trails for data changes (who/what/when) across heterogeneous storage and compute paths.

Tech stack

• Core: modern C++ (C++20, async + multithreaded primitives)
• Services & tooling: Go and Python (microservices, utilities, integration tests)

What we expect

• 5+ years of software engineering experience.
• Strong C++ skills (you’ll write core code).
• Working knowledge of Python and/or Go (you don’t have to be expert, but should be comfortable navigating them).
• Experience developing and/or operating high-load, distributed services.
• Production mindset: ability to use SSH, read logs/metrics/traces, and debug distributed systems behavior.
• Solid CS fundamentals: algorithms, data structures, concurrency basics.

Nice to have

• Experience with Big Data systems (YTsaurus/Hadoop/Spark/ClickHouse/Kafka-like ecosystems).
• Experience with multi-tenant platforms, schedulers, resource isolation, quotas, and reliability engineering.
• Strong performance engineering skills (profiling, lock contention, latency/throughput tradeoffs).

We conduct coding interviews as part of the process.

The role 

The Data Engineering team is responsible for building and maintaining robust data infrastructure that powers analytics, and business intelligence across Nebius. We design and implement scalable data pipelines, optimize data storage and processing, and enable data-driven decision making across the organization. The team works closely with product teams and business stakeholders to ensure alignment with company goals.We are looking for a Data Engineer to design, build, and maintain our data infrastructure and pipelines. You will work on processing large-scale datasets, optimizing data workflows, and enabling analytics capabilities that support our rapidly growing cloud platform.

Your responsibilities: 

• Design, develop, and maintain scalable data pipelines.
• Build and optimize data infrastructure.
• Implement data quality monitoring and validation frameworks.
• Optimize data storage, processing, and query performance for large-scale datasets.
• Design and implement data models for analytics and reporting use cases.
• Develop tools and automation to improve data engineering workflows and productivity.
• Ensure data governance, security, and compliance standards are met.
• Participate in on-call rotation to support production data systems.

Must-haves: 

• 3+ years of experience in data engineering or related roles.
• Experience building and maintaining data pipelines using orchestration tools (e.g., Airflow, Prefect, Dagster).
• Strong proficiency in SQL and solid programming skills in Python.
• Experience with distributed data processing frameworks (e.g., Apache Spark, or similar).
• Knowledge of data modeling principles and best practices.
• Understanding of data architectures and storage systems.

Nice-to-haves: 

• Experience with real-time data streaming platforms.
• Familiarity with Infrastructure as Code tools (Terraform, etc).
• Experience with containerization (Docker) and Kubernetes.
• Knowledge of data governance and privacy frameworks (GDPR, SOC2).
• Knowledge of data quality and observability tools (Great Expectations, etc.).

We conduct coding interviews as part of the process.

Team Description

The Data Engineering team is responsible for developing and maintaining the company’s data ingestion and processing pipelines. This role collaborates with data scientists, software engineers, business analysts, and technical leads to build reliable data infrastructure that supports decision-making for apparel brands and retailers. The position reports to the Technical Lead within the Data Engineering function.

Your Impact

• Contribute to the development and enhancement of data ingestion and processing pipelines
• Partner with product managers, data scientists, and engineers to build and maintain accurate and robust data infrastructure managing first- and second-party data
• Collaborate with technical leads and senior engineers on data architecture decisions and implementation strategies
• Support the team in delivering scalable solutions while learning and growing your expertise
• Work as part of a collaborative team that values creativity and continuous improvement
• Help build and maintain data pipelines that support AI and decision-making tools

Your Qualifications

• Commercial & proven experience in data engineering or related technical roles
• Proficiency in SQL and Python for data processing and pipeline development
• Experience with data warehousing concepts and ETL/ELT processes
• Hands-on experience with modern data platforms such as Snowflake, Databricks, or Redshift
• Familiarity with data orchestration tools like Apache Airflow, Prefect, or DBT
• Experience working with columnar formats like Parquet and Avro
• Understanding of data modeling, database optimization, and performance tuning
• Experience integrating data from various sources including APIs, FTP, and cloud storage

Preferred Skills

• Knowledge of streaming data processing and data APIs
• Knowledge of cloud platforms, particularly AWS
• Familiarity with infrastructure as code and CI/CD practices
• Experience with time series data

The Community You Will Be Part Of: 

Airbnb, a leader in travel and hospitality, is on a mission to create a world where anyone can belong anywhere. We offer unique accommodations and experiences, crafted and curated by locals. As we expand into new marketplaces for in-person experiences and services, we are on the lookout for driven and innovative individuals to help shape our growth trajectory.

The Difference You Will Make:

This Advanced Analytics role will develop and lead initiatives focused on ensuring we have the right international growth strategy. They will lead measurement, build tools, and develop insights leveraging a wide range of internal and external data sources to ensure the business is making the right decisions for both strategic direction and tactical execution.

The ideal candidate is someone with a strong data and analytics background. This person is curious, engaged, positive, and detail- and solutions-oriented. The world— and thus our industry— is changing quickly so the ideal candidate will be comfortable with responding promptly and changing course as needed when priorities and reporting needs shift. 

This role will be expected to be highly collaborative, and work regularly with multiple stakeholders and cross functional partners across Regional Teams, Policy, Supply, Finance, Data Science, Operations, Engineering, and Marketing.

The right candidate will already be recognized as a leader in the data and analytics field at this level, with a proven track record of using a range of strategies and disciplines to solve complex and ambiguous measurement problems for business teams. This person will also use their considerable experience with data and insights storytelling to influence large-scale strategic programs, and  be adept at leveraging AI technologies to accelerate and raise their craft.

You must be comfortable working in a global company, delivering counsel and guidance to senior leadership across the team and organization. The successful candidate will have solid experience creating internal advocacy and influencing multiple senior audiences on hard to communicate topics.

The Team You Will Join: 

The Global Markets & Policy Advanced Analytics team is building the data infrastructure, measurement systems, and self-service tools that will power Airbnb's international growth in 2026 and beyond. This is a greenfield opportunity to shape how global teams make nimbler decisions, moving from HQ-dependent reporting cycles to fast, local, data-driven action.

This role will partner directly with Global Markets regional teams combined with Finance and Supply to surface local insights, build scalable tooling, and drive measurable impact on core business metrics. The ideal candidate brings deep technical rigor, strong business instincts, and a bias toward proactively building things—not just analyzing them.

A Typical Day:                    

• Build and maintain self-service analytics tools and dashboards that help country teams monitor performance and act on local signals                                    
• Partner with regional leads to translate business questions into clear analytical frameworks and actionable insights                                                    
• Support measurement for local marketing programs — campaign tracking, incrementality testing, and MMM contributions                                                     
• Analyze international market data to identify and size growth opportunities across supply, demand, and behavioral signals                                               
• Collaborate with Engineering, Marketing, and Research to deliver on data and tooling needs within shared roadmaps                                                       
• Provide analytical support for local growth levers (incentives, promotions, merchandising) including targeting and measurement                                                                                                                                         

Your Expertise:                                                                                                                                                                                                                                                                                                                                 

• 4+ years in data science, analytics, or business intelligence — marketplace, e-commerce, or global consumer tech preferred
• Expert SQL; proficiency in Python or R                                                                                                                             
• Hands-on experience with A/B testing, causal inference, and incrementality measurement                                                                                  
• Experience building self-service data products used by non-technical stakeholders                                                                                      
• Familiarity with international / multi-market analytics contexts                                                                                                        
• Strong data storytelling — able to communicate findings clearly to business and senior audiences                                                                        
• Collaborative, low-ego, and execution-oriented; you measure success by the decisions your work influences                                                               
• Comfortable with modern data stack tools (dbt, Airflow, Tableau, Looker, or equivalent)                                                                                                                                                                                          

Nice to Have:                                                                                                                                                             

• Supply/demand or geo-based analytics experience                                                                                                                         
• Growth marketing analytics (SEM, paid social, performance channels)
• Familiarity with MMM, attribution, or campaign measurement frameworks                                                                                                   
• Awareness of AI/LLM trends and implications for organic traffic and demand      

Our Commitment To Inclusion & Belonging:

Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively-led people, and to develop the best products, services and solutions. All qualified individuals are encouraged to apply.

The Community You Will Be Part Of: 

Airbnb, a leader in travel and hospitality, is on a mission to create a world where anyone can belong anywhere. We offer unique accommodations and experiences, crafted and curated by locals. As we expand into new marketplaces for in-person experiences and services, we are on the lookout for driven and innovative individuals to help shape our growth trajectory.

The Difference You Will Make:

This Advanced Analytics role will develop and lead initiatives focused on ensuring we have the right international growth strategy. They will lead measurement, build tools, and develop insights leveraging a wide range of internal and external data sources to ensure the business is making the right decisions for both strategic direction and tactical execution.

The ideal candidate is someone with a strong data and analytics background. This person is curious, engaged, positive, and detail- and solutions-oriented. The world— and thus our industry— is changing quickly so the ideal candidate will be comfortable with responding promptly and changing course as needed when priorities and reporting needs shift. 

This role will be expected to be highly collaborative, and work regularly with multiple stakeholders and cross functional partners across Regional Teams, Policy, Supply, Finance, Data Science, Operations, Engineering, and Marketing.

The right candidate will already be recognized as a leader in the data and analytics field at this level, with a proven track record of using a range of strategies and disciplines to solve complex and ambiguous measurement problems for business teams. This person will also use their considerable experience with data and insights storytelling to influence large-scale strategic programs, and  be adept at leveraging AI technologies to accelerate and raise their craft.

You must be comfortable working in a global company, delivering counsel and guidance to senior leadership across the team and organization. The successful candidate will have solid experience creating internal advocacy and influencing multiple senior audiences on hard to communicate topics.

The Team You Will Join: 

The Global Markets & Policy Advanced Analytics team is building the data infrastructure, measurement systems, and self-service tools that will power Airbnb's international growth in 2026 and beyond. This is a greenfield opportunity to shape how global teams make nimbler decisions, moving from HQ-dependent reporting cycles to fast, local, data-driven action.

This role will partner directly with Global Markets regional teams combined with Finance and Supply to surface local insights, build scalable tooling, and drive measurable impact on core business metrics. The ideal candidate brings deep technical rigor, strong business instincts, and a bias toward proactively building things—not just analyzing them.

A Typical Day:                    

• Build and maintain self-service analytics tools and dashboards that help country teams monitor performance and act on local signals                                    
• Partner with regional leads to translate business questions into clear analytical frameworks and actionable insights                                                    
• Support measurement for local marketing programs — campaign tracking, incrementality testing, and MMM contributions                                                     
• Analyze international market data to identify and size growth opportunities across supply, demand, and behavioral signals                                               
• Collaborate with Engineering, Marketing, and Research to deliver on data and tooling needs within shared roadmaps                                                       
• Provide analytical support for local growth levers (incentives, promotions, merchandising) including targeting and measurement                                                                                                                                         

Your Expertise:                                                                                                                                                                                                                                                                                                                                 

• 4+ years in data science, analytics, or business intelligence — marketplace, e-commerce, or global consumer tech preferred
• Expert SQL; proficiency in Python or R                                                                                                                             
• Hands-on experience with A/B testing, causal inference, and incrementality measurement                                                                                  
• Experience building self-service data products used by non-technical stakeholders                                                                                      
• Familiarity with international / multi-market analytics contexts                                                                                                        
• Strong data storytelling — able to communicate findings clearly to business and senior audiences                                                                        
• Collaborative, low-ego, and execution-oriented; you measure success by the decisions your work influences                                                               
• Comfortable with modern data stack tools (dbt, Airflow, Tableau, Looker, or equivalent)                                                                                                                                                                                          

Nice to Have:                                                                                                                                                             

• Supply/demand or geo-based analytics experience                                                                                                                         
• Growth marketing analytics (SEM, paid social, performance channels)
• Familiarity with MMM, attribution, or campaign measurement frameworks                                                                                                   
• Awareness of AI/LLM trends and implications for organic traffic and demand      

Our Commitment To Inclusion & Belonging:

Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively-led people, and to develop the best products, services and solutions. All qualified individuals are encouraged to apply.

Role & Team

As a Data Engineering Manager, you will lead across several cross-functional teams, managing up to ~10 data engineers and GIS specialists. You will support teams working on data processing and pipelines to enable ML-powered product capabilities.

Our product teams are cross functional and will typically include product managers, designers, engineers and subject matter experts relating to the team’s domain.

You will be accountable for the technical direction and delivery outcomes of your area. You will challenge your teams to pursue ambitious goals while providing a high level of support — growing engineering talent and fostering a highly collaborative, team-based environment where people can do great work.

Your primary focus will be on building high-performing teams and driving results through others. You will not be expected to spend significant time doing hands-on coding; however, you will be expected to dive deep technically when required and leverage your experience to support strong technical decision-making.

Time zone requirement: Europe (GMT/WET, CET, EET) and Eastern North America (NST, AST, EST)

What You'll Do

• Enable our data engineers to become highly productive members of their cross-functional teams, empowering them to ship high-quality, reliable data platforms and pipelines on a daily basis.
• Grow the teams by attracting great talent from your network.
• Foster an inclusive and caring culture where everyone can do their best work.
• Provide regular 1:1 coaching and feedback to tap into the potential of all your team members and help them thrive and reach their aspirations.
• Be a strategic partner to product managers, machine learning engineers, and other stakeholders within your domains and ensure that we are making the correct data architecture and platform decisions that balance short and long term goals.
• Ensure suitability of the data architecture, pipelines, and data models within your domains to enable longer term product, analytics, and machine learning goals.
• Work closely with the other Product & Engineering leaders on strategy, technology and people, including improving our data engineering practices and introducing capabilities that can accelerate data access, reliability, and unlock new product and data-driven opportunities

Skills & Experience

• Experience working in a high growth scaleup environment
• Passionate about climate
• Product minded and able to demonstrate significant impact you have had on a business through the application of data platforms and data-driven systems
• 2+ years experience leading and managing data engineers across multiple teams
• Experience working with the following (or similar) technologies: Python, SQL, data pipeline orchestration tools (e.g., Airflow, Dagster), data warehouses/lakehouses, Postgres DB, data pipelines
• Ability to unblock your team by contributing at a senior engineering level (e.g., reviewing data models, debugging pipelines, guiding architecture). How hands-on you'll be in this role will depend on team size and maturity
• Comfortable working in a build it, run it environment (GCP, cloud run, grafana, k8s) - or similar, with an understanding of data reliability, observability, and pipeline operations
• Strong leadership skills with the ability to mentor and coach engineers at all levels
• Excellent communication skills and ability to collaborate effectively in a cross-functional team environment
• Passion for learning and staying updated with evolving technologies and industry trends, particularly in data engineering and data infrastructure

Nice To Have

• Experience working with maps/imagery/geospatial domains  

Note: We believe that all people are capable of great things. We encourage you to apply even if you do not meet all of the requirements that are listed within this job description.

What We Offer

• Competitive, location-specific compensation and benefits 
• Flexible, autonomous and collaborative working environment rooted in trust - we build our work days around our lives, not the other way around
• Home office stipend, coworking and ongoing education budgets 
• A company culture that genuinely embodies each of our core values
• To be part of truly mission-driven work that reduces wildfires, protects earth’s natural resources and helps solve our climate crisis

Role & Team

As a Senior Engineer, you will have a direct impact on the development of core geospatial products and internal data-processing and annotation tools at Overstory. 

Your breadth of skills will allow you to set a technical vision for the development of the web applications, ETL pipelines, and backend services owned by these teams.

You’ll partner with product managers, external resources, and your team members to execute this vision.

Time zone requirement: Europe (GMT/WET, CET, EET) and North America (NST, AST, EST, CST, MST, PST)

What You’ll Do

• Explore best practices to combine coding agents with software craftsmanship and teach other engineers these practices
• Optimize the orchestration of several object detection and image alignment models
• Design and maintain robust data and feature pipelines for millions of geospatial entities
• Contribute to the technical vision for the development of our internal annotation and QA web applications
• Work closely with your Product Manager to discover and propose new products to solve use cases such as anticipating the impact of an outage at a particular locatio

Skills & Experience

• 5+ years of experience as a senior software engineer 
• Skilled in designing ETL pipelines with near-real-time orchestration, in Python.
• Strong background building and maintaining scalable web-applications with React, FastAPI, PostgreSQL
• Experience with XP practices such as TDD or trunk based development. The team would be new to these practices
• Strong communication skills and ability to explain complex technical concepts to various audience

Nice To Have

• Experience with the Geospatial ecosystem such as PostGIS, Geopandas or Deck.GL
• Experience with Dagster, Airflow or similar orchestration frameworks
• Experience with building web-applications tailored for repeatable human or agentic workflows
• Experience working in B2B, in particular with electrical utilities

Note: We believe that all people are capable of great things. We encourage you to apply even if you do not meet all of the requirements that are listed within this job description.

What We Offer

• Competitive, location-specific compensation and benefits 
• Flexible, autonomous and collaborative working environment rooted in trust - we build our work days around our lives, not the other way around
• Home office stipend, coworking and ongoing education budgets 
• A company culture that genuinely embodies each of our core values
• To be part of truly mission-driven work that reduces wildfires, protects earth’s natural resources and helps solve our climate crisis

• Lead a team of Engineers, setting clear expectations and providing continuous feedback, coaching, and mentoring to ensure success and growth
• Working closely with feature owners, economy, liveops and other development teams to implement feature simulations and technological pipelines for Monopoly GO!
• Driving the implementation of systems that transform raw game data into analytical datasets for simulation and evaluation
• Ensuring technical performance is “Best-in-Class” throughout development
• Writing automated, high-quality tests (unit, integration, load, etc.) and working with QA teams to improve gameplay experience, curtail exploits and ensure that everything works smoothly
• Reviewing Technical Design Documents and provide feedback and guidance to other engineers
• Collaborating and communicating with cross-functional departments including: economy, liveops, qa, etc.
• Participating in a strong engineering team and culture
• Live game support & troubleshooting
• Contributing to technical plans and discussing the workflows and requirements with stakeholders
• Implementing tools to empower our liveops and game design teams in the creation of new and exciting game-play experiences
• Explore AI-assisted tools (LLMs, copilots, automation frameworks) to improve productivity, enhance analysis depth, and accelerate iteration cycles

• 6+ years as part of a professional game development team or other relevant area
• Experience managing people, driving 1:1s and performance reviews, doing follow ups and providing regular feedback to direct reports
• Experience driving and leading the technical execution of complex and interdisciplinary developments
• Mastery of C#, Python or another OO language, including experience with asynchronous and multi-threaded programming paradigms
• Hands-on experience with cloud computing (AWS, GCP...) and low-latency, high-scale distributed systems
• Strong computer science and engineering fundamentals (data structures, concurrency, design patterns, algorithms)
• Strong understanding of game framework architecture at scale, maintainability over time, robust API design, etc.
• Strong knowledge of application design best patterns and practices; understanding of maintainability concerns when designing and implementing applications
• Enjoy solving complex technical problems, especially in large systems
• Willingness to learn and work with new technologies
• You’re a great tech communicator who knows how to share knowledge with non-technical people
• You have a desire to seek out and take on user feedback to drive world class application development
• A passion for games

• Experience building your own games as personal projects or game jams
• Experience with AWS, Docker, Kibana, Ansible
• Experience with automated testing
• Solid experience with SQL and at least one data warehouse (BigQuery preferred)
• Knowledge of probability, applied statistics, and simulation techniques
• Experience with distributed processing tools: Spark, PySpark, or alternatives
• Experience building ETL/ELT pipelines using Airflow, DBT, or similar frameworks
• Experience in gaming, monetization, or game economy
• Familiarity with gameplay logic or LiveOps systems
• Experience developing internal libraries or tools for non-technical teams
• Curiosity for experimenting with AI systems to extend capabilities of existing pipelines or prototype new evaluation methods

At Scopely, we believe in creating a supportive work environment where everyone is treated with respect and inclusivity. We welcome individuals from all backgrounds and are committed to providing equal employment opportunities regardless of age, race, ethnicity, gender, sexual orientation, religion, disability, or any other protected status.

Are you a Senior Data Engineer with a passion to build, support & maintain digital marketing solutions that offers real value? Are you interested in working with the largest global brands and complex challenges in the media analytics space? Then you might be the Senior Data Engineer we’re looking for!

What will your day look like?

As our new Senior Data Engineer, you will become part of our growing Data Insights and Science team. Here, you will employ new technologies across multiple cloud platforms to help successful brands reach their next level in 1:1 retargeting, communication, and CRM. More specifically, your tasks include:

• Identify, collect, and integrate data from various sources by building high-quality data pipelines and data models for analytics and business intelligence (BI) purposes.
• Develop and optimize code to enable pipelines at minimum cost and ease of maintenance.
• Build monitoring procedures & tools to ensure solid ETL flows and data quality.
• Design processes and tools to correct ETL incidents.
• Collaborate with CRM developers, data scientists, data analysts, and product owners to ensure the supplied data supports the business initiatives.
• Consult our data analytics teams to ensure best practices on the technical use of data are followed.
• Design data architectures and collaborate in data migrations in cloud environments. 

Who are you going to work with?

You will join a team of highly skilled Architects, Data Scientists, and Consultants who are passionate about unlocking insights from data through analytics. You will also get to work closely with experts from other Technology, Creative, and Client Teams.

What do you bring to the table?

As a person, you have a team player mindset and an open-minded attitude. You can communicate ideas and technical topics honestly and clearly – also to non-experts – while respecting the views of others on the team. You are eager to understand and find solutions, allowing you to quickly adapt to changing situations and come up with new ideas.

At the same time, you solve problems in an analytical and pragmatic manner.

Moreover, you have:

• Experience in data engineering, big data, business intelligence, or data science. 
• Experience in Spark, Python, Scala, or similar.
• Excellent SQL skills enabling large-scale data transformation and analysis.
• A comprehensive understanding of cloud data warehousing, data pipelines and data transformation (extract, transform, and load) processes and supporting technologies such as Google Dataflow, Looker, DBT, EMR, CI/CD Pipelines, Airflow DAGs, and other analytics tools.
• Experience with cloud-based data infrastructures (Ideally GCP, but AWS or Azure would also suffice)
• Programming experience in Javascript, Python, or similar.
• Expertise in managing databases, including performance tuning, backup, and recovery.
• Solid knowledge of data quality management best practices, including data profiling, data cleansing, and data validation
• Knowledge of strengths and limitations of visualization tools (e.g. PowerBI, Tableau, Looker, etc.) in terms of data modeling in the visualization tools.
• Understanding of versioning control tools like GitHub to changes related to dbt models and transformations, ensuring that changes are tracked, documented, and reviewed by the appropriate stakeholders.
• Knowledge of applying data governance principles, policies, and practices that ensure data accuracy, consistency, and security
• Knowledge of Kubernetes would be an advantage

Personal skills:

• Strong desire to contribute towards keeping data tidy and well-organized
• Ability to think critically, identify issues autonomously, and propose corrective actions.
• Build great relationships with your team and stakeholders

A leader in personalized customer experiences

VML MAP is a world-leading Centre of Excellence that helps businesses humanize the relationship between the brand and the customer through hyper personalization at scale, marketing automation and CRM. With the brain of a consultancy, the heart of an agency and the power of technology and data, we work with some of the world's most admired brands to help them on their transformation journey to becoming truly customer-centric. Together, we are 1000 + technology specialists, data scientists, strategic thinkers, consultants, operations experts, and creative minds from 55+ nationalities.

A global network

We are part of the global VML network that encompasses more than 30,000 employees across 150+ offices in 60+ markets, each contributing to a culture that values connection, belonging, and the power of differences. 

#LI-EMEA

As a Staff Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

This role blends hands-on technical work with strategic thinking. You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Identify high-impact ML opportunities and influence stakeholders to prioritize and support these initiatives.
• Design and develop scalable machine learning models — including classifiers, regressors, and rule-based systems — to solve real-world problems.
• Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment.
• Translate complex technical concepts into clear insights for both technical and non-technical stakeholders.
• Set and guide technical direction across ML projects, ensuring technical best practices as well as alignment with business goals.
• Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.

We're looking for someone with:

• Master’s degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field.
• 7+ years of industry experience building and deploying ML models at scale.
• Proven ability to lead cross-functional technical initiatives and influence engineering strategy.
• Proficiency in Python (with libraries like PyTorch, XGBoost, Scikit-learn) and SQL.
• Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar.
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies.
• A track record of shipping production-level ML products and maintaining high code quality.
• Excellent problem-solving skills and ability to scope and disambiguate complex ML projects into clear, achievable milestones.

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects or publications in ML conferences.

As a Staff Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

This role blends hands-on technical work with strategic thinking. You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Identify high-impact ML opportunities and influence stakeholders to prioritize and support these initiatives.
• Design and develop scalable machine learning models — including classifiers, regressors, and rule-based systems — to solve real-world problems.
• Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment.
• Translate complex technical concepts into clear insights for both technical and non-technical stakeholders.
• Set and guide technical direction across ML projects, ensuring technical best practices as well as alignment with business goals.
• Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.

We're looking for someone with:

• Master’s degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field.
• 7+ years of industry experience building and deploying ML models at scale.
• Proven ability to lead cross-functional technical initiatives and influence engineering strategy.
• Proficiency in Python (with libraries like PyTorch, XGBoost, Scikit-learn) and SQL.
• Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar.
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies.
• A track record of shipping production-level ML products and maintaining high code quality.
• Excellent problem-solving skills and ability to scope and disambiguate complex ML projects into clear, achievable milestones.

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects or publications in ML conferences.

As a Staff Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

This role blends hands-on technical work with strategic thinking. You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Identify high-impact ML opportunities and influence stakeholders to prioritize and support these initiatives.
• Design and develop scalable machine learning models — including classifiers, regressors, and rule-based systems — to solve real-world problems.
• Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment.
• Translate complex technical concepts into clear insights for both technical and non-technical stakeholders.
• Set and guide technical direction across ML projects, ensuring technical best practices as well as alignment with business goals.
• Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.

We're looking for someone with:

• Master’s degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field.
• 7+ years of industry experience building and deploying ML models at scale.
• Proven ability to lead cross-functional technical initiatives and influence engineering strategy.
• Proficiency in Python (with libraries like PyTorch, XGBoost, Scikit-learn) and SQL.
• Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar.
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies.
• A track record of shipping production-level ML products and maintaining high code quality.
• Excellent problem-solving skills and ability to scope and disambiguate complex ML projects into clear, achievable milestones.

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects or publications in ML conferences.

As a Staff Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

This role blends hands-on technical work with strategic thinking. You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Identify high-impact ML opportunities and influence stakeholders to prioritize and support these initiatives.
• Design and develop scalable machine learning models — including classifiers, regressors, and rule-based systems — to solve real-world problems.
• Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment.
• Translate complex technical concepts into clear insights for both technical and non-technical stakeholders.
• Set and guide technical direction across ML projects, ensuring technical best practices as well as alignment with business goals.
• Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.

We're looking for someone with:

• Master’s degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field.
• 7+ years of industry experience building and deploying ML models at scale.
• Proven ability to lead cross-functional technical initiatives and influence engineering strategy.
• Proficiency in Python (with libraries like PyTorch, XGBoost, Scikit-learn) and SQL.
• Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar.
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies.
• A track record of shipping production-level ML products and maintaining high code quality.
• Excellent problem-solving skills and ability to scope and disambiguate complex ML projects into clear, achievable milestones.

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects or publications in ML conferences.

As a Staff Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

This role blends hands-on technical work with strategic thinking. You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Identify high-impact ML opportunities and influence stakeholders to prioritize and support these initiatives.
• Design and develop scalable machine learning models — including classifiers, regressors, and rule-based systems — to solve real-world problems.
• Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment.
• Translate complex technical concepts into clear insights for both technical and non-technical stakeholders.
• Set and guide technical direction across ML projects, ensuring technical best practices as well as alignment with business goals.
• Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.

We're looking for someone with:

• Master’s degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field.
• 7+ years of industry experience building and deploying ML models at scale.
• Proven ability to lead cross-functional technical initiatives and influence engineering strategy.
• Proficiency in Python (with libraries like PyTorch, XGBoost, Scikit-learn) and SQL.
• Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar.
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies.
• A track record of shipping production-level ML products and maintaining high code quality.
• Excellent problem-solving skills and ability to scope and disambiguate complex ML projects into clear, achievable milestones.

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects or publications in ML conferences.

About MyHeritage

MyHeritage is a global leader in family history and consumer DNA testing, enriching the lives of millions worldwide by enabling them to uncover more about themselves and where they belong. With a suite of intuitive products, billions of historical records, an affordable at-home DNA test, and AI-powered photo tools, MyHeritage creates a meaningful discovery experience that is deeply rewarding.

Position Overview

We are looking for a Director of Science to guide MyHeritage’s scientific research and development in genetics. This role is central to building innovative algorithms, driving rigorous data analysis, and advancing the scientific methodologies that power our DNA products - including ethnicity estimation, relationship inference, local ancestry, and more. 

The ideal candidate combines deep scientific expertise in population and statistical genetics with strong leadership and a proven ability to execute in a dynamic, consumer-facing environment.

Your work will directly impact millions of users worldwide, helping them discover their ancestry and connect with family through science. You’ll apply cutting-edge methods to analyze millions of DNA samples and family trees, leveraging one of the largest consumer genetics datasets in the world.

Key Responsibilities

• Scientific leadership: Define and execute the scientific roadmap aligned with company goals.
• Team leadership: Manage and mentor a team of senior scientists and data scientists. Provide technical direction, support professional growth, and foster a culture of scientific excellence.
• Algorithmic research: Lead the design, development and optimization of scalable, production-ready algorithms and pipelines including ancestry inference, relationship classifiers, imputation and phasing of genetic data and additional models to support genetic genealogy.
• Collaboration and communication: Work closely with engineering, product, and data teams to translate research outcomes into user-facing features and tools. Clearly communicate scientific concepts and progress internally and externally.

The ideal candidate will have:

• Strong, hands-on experience in human population genetics and analysis of genotyping and/or sequencing data is essential. We are specifically looking for candidates with a demonstrated track record in this domain.
• Ph.D. in Genetics, Computational Biology, Bioinformatics, or a related quantitative field.
• Experience with population genetics and analysis of genotyping or sequencing data. 
• 5+ years of experience in scientific research, including 3+ years in a leadership role.
• Strong background in:
  1. Analysis of genotyping or sequencing data
  2. Genomic data pipelines and bioinformatics tools.
  3. Machine learning or statistical modeling applied to biological data
• Experience working with large-scale genomic datasets.
• Proficiency in Python and familiarity with production-scale computational pipelines. Familiarity with tools such as Snakemake, Airflow, or other pipeline managers is an advantage.
• Excellent written and verbal communication skills.
• Demonstrated ability to lead projects and collaborate effectively across disciplines.
• Experience with AWS is an advantage.

Why Join Us?

• Join a purpose-driven company helping people discover their heritage through the power of science.
• Be part of a collaborative, international team driven by innovation and scientific discovery.
• Enjoy a hybrid work environment with flexibility and autonomy.

Additional Information

• This role involves remote management of an Israel-based team.
• The position includes occasional travel to Israel, as needed.

#LI-Hybrid

At Rockstar Games, we create world-class entertainment experiences. 

Become part of a team working on some of the most rewarding, large-scale creative projects to be found in any entertainment medium - all within an inclusive, highly-motivated environment where you can learn and collaborate with some of the most talented people in the industry. 

Rockstar is on the lookout for a talented Technical Artist who possess a passion for technology and innovation. 

This is a full-time, permanent and in-office position based out of Rockstar’s unique game development studio in the heart of Leeds, England. 

WHAT WE DO

• The Rockstar Games Technical Art team creates, maintains and enhances tools and workflows for artists across our Art, Animation, and VFX teams.  
• We create and maintain photogrammetry tools and pipelines, and support live scan sessions.  
• We drive innovation and efficiency in our pipelines by continuously researching and developing new technology.  

RESPONSIBILITIES

• Design, develop, and support tools and pipelines in DCC and standalone contexts.  
• Develop automated solutions for data processing.   
• Stay informed on the latest technology and research related to the art of data pipelines and machine learning.  
• Write clear and detailed documentation for tools and workflows. 
• Diagnose and solve technical issues affecting artists.  

REQUIREMENTS

• 5+ years of experience as a Technical Artist, Technical Director, Tools Programmer, or similar.  
• Experience developing tools for DCCs like Houdini, Zbrush, Agisoft, or Metashape. 
• Experience working through a full game production cycle or long-term project. 
• A strong understanding of Photogrammetry workflows and software.  
• Proficiency in Python. 
• Experience with build systems like Jenkins, Airflow, or Celery.   
• An understanding of a 3D asset pipeline and color management.   
• Ability to think creatively and develop innovative solutions to complex problems. 

PLUSES

Please note that these are desirable skills and are not required to apply for the position.

• A degree in Computer Science or related discipline.  
• C++ programming skills.  
• Experience working with package management and deployment systems like Rez or PyPi.  
• Experience with ML based photogrammetry workflows like Gaussian Splats or Nerfs.

HOW TO APPLY

Please apply with a resume and cover letter demonstrating how you meet the skills above. If we would like to move forward with your application, a Rockstar recruiter will reach out to you to explain next steps and guide you through the process.

Rockstar is committed to creating a work environment that promotes equal opportunity, dignity and respect.  In line with this commitment, Rockstar will provide accommodations to job applicants with disabilities (or on the basis of any other protected ground under human rights legislation) during the recruitment process, as well as to any Rockstar employees in order for them to perform the essential functions of their roles.  Rockstar will comply with its obligations under the Human Rights Code and the Accessibility for Ontarians with Disabilities Act and will provide accommodations as long as such accommodations do not pose an undue hardship on Rockstar.  If you need more information about Rockstar’s accommodation policies or process, or need to request an accommodation, please notify your recruiter during the interview process.  

If you’ve got the right skills for the job, we want to hear from you. We encourage applications from all suitable candidates regardless of age, disability, gender identity, sexual orientation, religion, belief, race, or any other protected category.

#LI-AS1

P-1534

At Databricks, we are passionate about enabling data teams to solve the world's toughest problems. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. 

As an Engineering Manager, you will work with your team to build mission-critical Lakebase services on the Databricks Platform at scale.

Key responsibilities include:

• Drive continuous delivery within a team of experts in storage technology, distributed systems and Rust.
• Manage the development and rollout of storage services that host millions of customer databases across dozens of regions
• Partner with peer engineering teams across Databricks to co-evolve Lakebase services with our global infrastructure.
• Lead operational excellence in 24/7 operation of our system

The impact you will have:

• Hire great engineers to build an outstanding team.
• Support engineers in their career development by providing clear feedback and develop engineering leaders.
• Ensure high technical standards by instituting processes (architecture reviews, testing) and culture (engineering excellence).
• Work with engineering and product leadership to build a long-term roadmap.
• Coordinate execution and collaborate across teams to unblock cross-cutting projects.

What we look for:

• Experience with building and shipping storage systems where correctness and performance are essential
• BS (or higher) in Computer Science, or a related field
• 2+ years of experience building and leading a team of engineers working in a related system
• Experience with build, release and deployment infrastructure technologies such as Spinnaker, Jenkins, Airflow, Docker, Kubernetes, Terraform, Bazel, etc.
• Ability to attract, hire, and coach engineers who meet the Databricks hiring standards - can up level existing team via hiring top-notch senior talent, growing leaders and helping struggling members; can gain trust of the team and guide their careers
• Comfort working on cross-functional projects with the ability to deeply understand product and customer personas

At JetBrains, code is our passion. Ever since we started, back in 2000, we’ve been striving to make the strongest, most effective developer tools on earth. Today, AI-powered assistance and agents are becoming a core part of how developers work in our IDEs.

We’re building multi-step coding agents that can understand large codebases, plan changes, call tools, and iterate with the user. As a Research Engineer in the Agentic Models team, you’ll be responsible for the models, training loops, and evaluation pipelines that power these agents.

You’ll work at the intersection of SFT and RL-style post-training, and product-driven evaluation, using our distributed GPU and MapReduce clusters to ship models into JetBrains products.

As part of our team, you will:

• Design, implement, and maintain SFT and RL post-training pipelines for multi-step coding agents.
• Train and adapt LLMs for agent workflows, including planning, tool use, and multi-step interactions inside JetBrains IDEs.
• Build and develop evaluation and simulation environments where coding agents can act, be measured, and compared on realistic developer tasks.
• Design evaluation frameworks and metrics for agent behavior, analyze traces and logs, and close the loop from evaluation back into training, data, and reward design.
• Analyze training and evaluation results to propose and implement improvements to model architectures, training recipes, and datasets.
• Work with large-scale infrastructure, including distributed training on GPU clusters and large MapReduce-style data processing for pre-training and fine-tuning datasets.
• Collaborate closely with research, product, and infrastructure teams to turn high-level product visions into concrete models, experiments, and shipped features. 

We’ll be happy to bring you on board if you have:

• Extensive hands-on experience training LLMs (pre-training, fine-tuning, or post-training) in a research or production setting.
• Deep expertise in modern deep learning frameworks such as PyTorch, and specialized LLM training stacks (e.g. Megatron, NeMo, verl, or similar).
• Strong theoretical and practical understanding of LLM fundamentals: architectures, tokenization, data pipelines, batching, mixed precision, distributed training, and debugging unstable runs.
• The ability to own projects end to end, starting from a high-level problem or product pain point and overseeing it through the design, experimentation, implementation, and iteration phases.
• A product-aware mindset – you care about how developers actually use agents and can translate product needs and failure modes into modeling and evaluation work.
• At least 3 years of Python experience writing clean, maintainable code in modern ML codebases.

Our ideal candidate would have experience with:

• ML orchestrators and workflow tools such as Kubeflow, Dagster, Airflow, ZenML, and/or job schedulers like Kubernetes or SLURM.
• Large-scale data and training pipelines, e.g. MapReduce-style clusters, multi-node GPU training, or workloads on the order of 1M+ CPU/GPU hours.
• Designing and maintaining evaluation pipelines for LLMs or agents, including metrics, dashboards, experiment tracking, and automated regression checks.
• AI agent development, such as tool-using agents, planners, or multi-step coding workflows, and familiarity with agentic frameworks or patterns.
• Experiment tracking and observability using tools like Weights & Biases, MLflow, Langfuse, or similar.
• Inference optimization and serving optimized models in production.

#LI-KP1

At Rockstar Games, we create world-class entertainment experiences. 

Become part of a team working on some of the most rewarding, large-scale creative projects to be found in any entertainment medium - all within an inclusive, highly-motivated environment where you can learn and collaborate with some of the most talented people in the industry. 

Rockstar Games is on the lookout for a skilled Analytics Engineer with strong software development skills who is passionate about games and big data.

This is a full-time, permanent and in-office position based in Rockstar’s unique game development studio in the heart of Leeds, England. 

WHAT WE DO

• The Rockstar Games Analytics team provides insights and actionable results to a wide variety of stakeholders across the organization in support of their decision making.
• We partner with multiple departments across the company, leveraging analytics to measure and improve on the success and health of our games.
• We collaborate as a distributed team to develop innovative data pipelines, data products, data models, reports, analyses, and machine learning applications.
• The Analytics Architecture vertical within the Analytics team is tasked with building out high impact data models and internal tooling to support the wider Analytics team.

RESPONSIBILITIES

• Partner with Data Scientists and business stakeholders to understand analytical needs and translate them into robust data infrastructure and workflows for the global Analytics team to leverage for deriving insights. Collaborate with the Data Engineering team to design data models and data pipelines. 
• Design, develop and maintain scalable data pipelines. Monitor performance and optimize data systems for high performance and reliability.
• Collaborate with the Data Integrity team to build a Data Quality Framework that ensures production data meets SLAs for key stakeholders and business processes.  
• Perform timely Root Cause Analysis to troubleshoot data-related issues; assist in implementation of code and process fixes.
• Provide thought leadership and collaborate with other team members to continue to scale our architecture to evolve for the needs of tomorrow. Contribute to the establishment of best practices including code standards, version control, documentation, testing, and review processes.
• Develop and support CI/CD processes using Terraform, Github, TeamCity, Octopus, etc.
• Define and implement monitoring and alerting policies for data solutions. 

REQUIREMENTS

• 3+ years of experience in analytics engineering or data engineering Python. 
• Proven track record in building, monitoring, and optimizing large-scale data systems and analytics infrastructure.
• Experience working in Databricks & Azure environment (or other cloud equivalents).  
• Experience working with pipeline scheduling tools such as Airflow & Astronomer.
• Experience working with CI/CD tools such as TeamCity, Terraform, Github, Octopus.
• Bachelor’s degree or equivalent in an engineering or technical field such as Computer Science, Mathematics, Statistics, or strong quantitative and software background. 
• 3+ years of hands-on experience in using advanced SQL queries (analytical functions), experience in writing and optimizing highly efficient SQL queries.
• Ability to push the frontier of technology and freely pursue better alternatives.

PLUSES

Please note that these are desirable skills and are not required to apply for the position.

• Knowledge of software coding practices across the development lifecycle, including agile methodologies, coding standards, code reviews, source management, build processes, testing, and operations.
• Streaming or NRT modeling experience.
• Experience with PySpark.
• Passion for technical mentorship. 

HOW TO APPLY

Please apply with a CV and cover letter demonstrating how you meet the skills above. If we would like to move forward with your application, a Rockstar recruiter will reach out to you to explain next steps and guide you through the process.

Rockstar is committed to creating a work environment that promotes equal opportunity, dignity and respect. In line with this commitment, Rockstar will provide reasonable accommodations to qualified job applicants with disabilities during the recruitment process in order for such applicants to be considered for the position for which they are applying, as well as to qualified employees to enable them to perform the essential functions of their roles. If you need more information about Rockstar’s reasonable accommodation policies or process, or need to request an accommodation, please notify your recruiter during the interview process. 

If you’ve got the right skills for the job, we want to hear from you. We encourage applications from all suitable candidates regardless of age, disability, gender identity, sexual orientation, religion, belief, race, or any other protected category.

#LI-CG1

We are currently looking for a Senior Data Engineer to join us remotely in the UK as we plan to set up a hub in the UK in the following 6-12 months to enable our GTM strategy. You will work with our EMEA and US customers to help them solve their data engineering, ML, ML Ops, and cloud migration puzzles.

"As a Data Engineer at DATAPAO, you will have the unique opportunity to push the boundaries of technology and lead the way in how data transforms the world. We work with customers who want to innovate and change their industries. So it never gets boring! Sorting out a complex technical puzzle, setting up a new data architecture from scratch, or guiding our customers through adopting the latest technologies is our routine here. Hence, we'd expect you to be more than just a (data) engineer. We'd expect you to breathe and live data, to thrive for knowledge and excellence, and to be keen to build something enduring and impactful for our customers. We'd expect you to be a (data) Paoneer!"  (Máté Gulyás, CEO)

What will you do?

As a Senior Data Engineer, you are expected to deliver (across industries) on some of our most complex projects - individually or by leading small delivery teams. Although we work for the biggest multinational companies where years-long behemoth projects are the norm, our projects are fast-paced, typically 2 to 4 months long. Most are delivered using Apache Spark/Databricks on AWS/Azure and require you to directly manage the customer relationship alone or in collaboration with a Project Manager. Additionally, at this seniority level, we expect you to compound your impact across the team and the organization - by supporting our pre-sales process, mentoring more junior engineers, and contributing to hiring best-in-class data talent.

What does it take to fit the bill?

Technical Expertise

 Consulting & Client-Facing Skills

Operational Readiness & Soft Skills

• In a fast-paced consulting environment, where no two days are the same, flexibility and problem-solving comes naturally to you. Furthermore, resilience and thriving on challenges are essential traits in our industry;

What do we offer?

IMPORTANT NOTE: Given that we are still sorting our ways out in the UK, this list will likely get an update in the following months, and we're also committed to growing it with us over time.

The gross salary for this position ranges from 70.000 to 100.000 GBP/year, depending on your assessed seniority level during the selection process.

Please note that currently we are unable to support transfer or offer full sponsorship for Skilled Worker visas as we’re just setting foot into the UK.