Location: UK or Portugal

Department: Engineering

Employment Type: Permanent

About YLD:

Our goal is to help our clients keep moving forward. We work with them to build the skills and capabilities they need to stay ahead of the competition, and we know our success only happens when they succeed. We’re a team of curious, talented people, and we’re committed to making YLD a place where great people want to work, grow, and stay. We’re a remote-first consultancy specialising in software engineering, product design, and data. Our teams are based across London, Lisbon, and Porto, and we create digital solutions that continue to deliver value long after our work is done.

About the role:

As a Senior Data Engineer, you’ll be part of a client team building innovative products. We expect you to be curious, passionate, driven, and enthusiastic. You’ll be proficient at problem-solving, and crafting scalable, resilient, and fault-tolerant architectures. You’ll be an effective communicator and a team player. You and your team will move in a fast-paced engineering environment, achieving outstanding results and delivering exciting projects.

About you:

A YLD Senior Data Engineer is a highly skilled engineer who spans across data engineering and analytics engineering, and knows where the boundaries sit. You've built pipelines and transformation layers. You've inherited messy data projects and made them good. You have opinions about how data teams should work and the humility to adapt them to context.

You have a real passion for the craft and are an active learner who gets excited by the variety that consultancy brings: different clients, different stacks, different problems.

Your day-to-day responsibilities will look like this:

• Delivering data work on client engagements: pipelines, transformation layers, models, and the infrastructure around them
• Adapting to different client contexts, legacy warehouse migrations, greenfield lakehouses, struggling transformation layers that need rescuing
• Defining and evolving our internal standards for data work: testing, documentation, project structure, code review
• Mentoring and growing data engineers across the company
• Showing up for client team moments (internal speakers, learning sessions, social events) to build strong working relationships.
• Contributing to proposals and helping shape engagements during the sales process
• Representing YLD externally through writing, speaking, and contributing to the wider data community.

You’ll have the following skills and experience:

• SQL as engineering: You write SQL that's reviewable, testable, and performs at scale. You understand query planning, engine quirks, and how materialisation choices affect cost and performance
• Python for data: You write typed, testable Python for pipelines and tooling. You've built and maintained production data systems, not just notebooks
• Data modelling: You've made deliberate choices between dimensional, Data Vault, normalised, and denormalised designs. You can explain those trade-offs in terms of flexibility, query performance, and maintainability
• Transformation architecture: You design transformations that are idempotent, incremental, and dependency-aware. You think in DAGs, not scripts
• Pipeline design: You understand batch, streaming, and micro-batch trade-offs. You consider latency, complexity, cost, and reprocessability. And choose the right approach for the problem
• Data testing: You write tests as code, schema contracts, assertions, and transformation logic. You know what to catch at build time vs. defer to observability
• Data observability: You treat data reliability like site reliability. Measurable indicators, alerting, incident response, and root cause analysis
• CI/CD for data: You version, test, and deploy pipelines like software. Environment promotion, rollback strategies, and infrastructure as code for reproducible environments
• Data governance: You've implemented lineage, cataloguing, sensitive data classification, or access control. Not just built pipelines, but made them auditable and secure
• Cost-conscious: You've optimised warehouse spend, storage strategies, or job efficiency. You treat compute as a resource to manage, not ignore
• Platform fluency: You've worked across the modern stack. Orchestration (Airflow, Dagster), warehouses (Snowflake, BigQuery, Databricks), ingestion (Fivetran, Airbyte, custom), and transformation (dbt, Spark)
• Cross-functional fluency: You translate engineering constraints into business terms and negotiate realistic commitments

You’ll be:

• Self-motivated and proactive
• Comfortable with ambiguity, experimentation, and iteration
• Collaborative by default. You work well in teams and across teams
• Analytical and detail-oriented in your approach to problems
• Comfortable context-switching across clients, stacks, and problem types
• Willing to shape engagements, not just deliver them: contributing to proposals, scoping, and client relationships
• Active in the wider data community: writing, speaking, or open-source contribution
• Genuinely committed to diversity, equity, and inclusion

Benefits you'll get:

• Company Private Health care
• Enhanced fully paid maternity and paternity leave for up to 6 months
• Company’s Pension Scheme (UK Only)
• 25 days annual holiday (excluding Public Holidays)
• £2000/€2000 annual allowance for Training/Conferences
• £300/€300 annual allowance for additional hardware
• Wellbeing & Performance Support via Oli including Therapy and Coaching
• Discretionary Bonus (depending on Company performance and results)
• Company laptop
• Generous referral schemes

Our typical Recruitment Process looks like this:

• 1st Interview with someone from the Talent team (30-45 Minutes)
• Technical Interview with our Senior Developers (90 Minutes)
• Call with the Head of Engineering (60 Minutes)

Get a glimpse of what it's like working at YLD! 🤳

Check out our LinkedIn page

We live and breathe our values, and know you will too:

• Growing every day
• Including everyone
• Relationships built on honesty and ethics
• Inspiring solutions
• Winning together

We’re an equal-opportunity employer and value diversity in all its forms. We do not discriminate based on race, religion, colour, national origin, gender, sexual orientation, pregnancy or maternity, age, marital status, or disability. We also offer a remote-first working environment, with flexible working and work–life balance as standard for all employees.

Let's start with the role

As a GenAI Senior Data Scientist, you will be at the forefront of our AI developments,  analyzing data, building Gen AI powered applications and/or agentic architectures. You will be responsible for selecting the right LLMs, performing prompt engineering and model fine-tuning to support our AI products. You will collaborate closely with cross-functional tech teams to deliver impactful, production-grade AI solutions. The ideal candidate has a deep understanding of LLMs and agentic architectures, along with proven experience building and deploying such applications in production environments.

As a GenAI Senior Data Scientist you will:

• Translate product requirements to AI problems and identify areas where AI can drive the most business impact;
• Analyze data to uncover significant patterns and generate actionable insights;
• Architect agentic and GenAI systems;
• Select appropriate models for the right tasks and tune their performance;
• Perform exploratory data analysis (EDA and feature engineering to support the modeling process;
• Apply best practices on model selection, parameter tuning etc.;
• Run comparative experiments for model training;
• Analyze ML metrics to evaluate different potential solutions;
• Manage the full lifecycle of AI features, from data collection to model design and to implementation and optimization in production;
• Mentor and guide junior members, sharing knowledge and leading complex projects. 

What you’ll bring:

Must have:

• Hands on experience with GenAI and Agentic applications;
• Extensive hands-on experience in delivering AI driven products to production;
• Experience in scaling and optimisation of AI applications;
• Knowledge of machine learning algorithm and respective theory;
• Knowledge of the Python machine learning ecosystem and solid software background in OOP;
• Strong skills in teamwork, communication, and analytical thinking;
• Fluency in English, both oral and written.

Nice to have:

• Experience with Big Data tools  (Spark/PySpark) and cloud environment, ideally Azure / Databricks;
• Previous experience in LLM fineturing;
• Previous experience with LLM serving;
• Knowledge of Deep learning. 

Kaizen Gaming Perks

• 🕑 Hybrid way of working
• 🏃 A buddy will support you with your onboarding
• 💸 Competitive pay & bonus scheme
• ⭐Developmental 360° feedback framework
• 💰 Monthly meal allowance
• 👩‍⚕️ Private health insurance for you and your family
• 📚 Unlimited access to Udemy & continuous training
• 👨‍👩‍👧‍👦 Family Support.
• #LI-Hybrid
• #LI-ap1

Let's start with the role

As the team lead of an AI product team you will lead and guide the team's efforts in delivering high impact AI products. You will be at the core of AI developments, analyzing data, building machine learning models, collaborating with the tech team to create successful AI products. 

The ideal candidate will combine a deep understanding of machine learning algorithms and proven extensive experience in building  ML/AI powered applications in production along with previous experience of leading and managing AI teams.

As a Data Science Team Lead, you will:

• Manage a team of 5 to 7 skilled data scientists and machine learning engineers. 
• Translate product requirements to machine learning problems and identifying areas where AI can have the most business impact;
• Analyze data to identify significant patterns and generate insights;
• Perform exploratory data analysis (EDA) and feature engineering to support the modeling process;
• Implement best practices on model selection, parameter tuning and optimization.
• Run comparative experiments for model training;
• Analyze ML metrics to evaluate different potential solutions;
• Manage the full lifecycle of AI features, from data collection to model design and to implementation and optimization in production environments;
• Mentor and share knowledge with more junior members and guide the team through complex projects. 

What you’ll bring

• Previous experience as a people manager;
• Expert knowledge of machine learning algorithm and their theoretical foundations;
• Extensive hands-on experience in delivering machine learning models to production;
• Experience in developing MLOps solutions;
• Knowledge of Deep learning;
• Proficiency in Python’s machine learning ecosystem and solid software engineering skills (OOP);
• Strong skills in teamwork, communication, and analytical thinking;
• Fluency in English, both oral and written.

• Experience with Big Data tools  (Spark/PySpark) and cloud environment, ideally Azure / Databricks;
• Previous experience in pricing optimisation;
• Previous experience in forecasting;
• Previous experience in the betting industry.

Kaizen Gaming Perks

• 🕑 Hybrid way of working
• 🏃 A buddy will support you with your onboarding
• 💸 Competitive pay & bonus scheme
• ⭐Developmental 360° feedback framework
• 💰 Monthly meal allowance
• 👩‍⚕️ Private health insurance for you and your family
• 📚 Unlimited access to Udemy & continuous training
• 👨‍👩‍👧‍👦 Family Support
• #LI-HYBRID
• #LI-AP

Let's start with the role

At Kaizen, we aim to leverage the capabilities offered by modern AI algorithms to continuously improve our product and business. Kaizen’s AI department consists of product, platform and research teams and lies at the core of that mission. We develop AI products for various applications, ranging from detection of fraudulent activity to personalized gaming recommendations, and from AI-powered co-pilots to CRM optimizations. 

We build our teams to be self-driven, autonomous and cross-functional. Within these teams, you will have the opportunity to work with Data Scientists, Machine Learning Engineers, Data Engineers, Data Analysts, Product Owners and Agile Delivery Leads.

As a Machine Learning Software Engineer, you will focus on delivering robust and scalable Machine Learning pipelines and you will support the operationalization of Machine Learning models. 

• Design, develop and maintain scalable model training and inference pipelines in a distributed production environment;
• Design, develop and maintain microservices in Python that host and serve ML models on production;
• Design, develop and maintain internal tools that enable CI/CD/CT, performant feature engineering, experiment tracking, data and model versioning.

What you’ll bring

• Ηands-on experience in software development using Python on a production environment;
• Good understanding of:
• Software system design; 
• The ML project lifecycle;
• MLOps concepts and tools such as MLflow or Kubeflow;
• ML algorithms and models.
• Experience with cloud providers like Microsoft Azure or AWS;
• Experience with PySpark and Databricks.

Kaizen Gaming Perks

• 🕑 Hybrid model of working
• 🏃 A buddy will support you with your onboarding
• 💸 Competitive salary package and bonus scheme
• 👩‍⚕️ Health and life insurance for you and your family
• 💰 Monthly meal allowance 
• ⭐ Developmental 360° feedback framework
• 📚 Unlimited access to Udemy & continuous training
• 🚗 Commuting allowance
• 👨‍👩‍👧‍👦 Family Support
• #LI-Remote

Let's start with the role

As a Product Analyst on the Sportsbook Product team, you will help shape how product performance is measured by defining the right questions to ask, and scaling analytics methods and tools to support our growing business. You will drive high-quality, high-velocity decisions for Sportsbook’s diverse product teams while directly reporting to our Head of Sportsbook Product.

We’re looking for an experienced product analyst to be responsible for defining and tracking KPIs, supporting strategic initiatives, evaluating A/B tests through 3rd-party experimentation platforms, and aggregating data from multiple sources—from GA4 to our big data lake of transactional data—to help the team achieve a 360° view of our customers.

Our ideal candidate is a great storyteller, using data to support product roadmaps and ensure that any new feature launched is continually improved through data insights and impactful narratives. Your input will be crucial in decision-making across all major product strategies and initiatives for new feature development and ongoing product improvements. 

• Work alongside the Sportsbook Product Team to support our dynamic and fast-paced Product roadmap, providing insights to help achieve our Product vision by defining, monitoring and reporting on KPIs and product metrics.
• Leverage the transactional data lake to connect customer journeys with business outcomes.
• Support our experimentation process by analyzing A/B test results and helping expand our internal experimentation framework.
• Implement GA4 tagging to map all user journeys, identify and highlight customer pain points across products, and work closely with the Sportsbook product management team to prioritize these in the roadmap.
• Synthesize data from a variety of sources, in close collaboration with BI and other stakeholders, to develop dashboards, reports, and visualizations (e.g., funnels in Looker Studio or other internal tools) that drive understanding and inform action across the Sportsbook vertical.
• Ensure data quality, consistency, and governance across all analytics initiatives to maintain trust and reliability in insights.
• Use data analytics, data visualization, and data modeling techniques to extract actionable insights that will facilitate prioritization and product strategy formulation.
• Advise and inform the Sportsbook team at each stage of the product lifecycle—from supporting product and design teams during discovery to measuring the impact of features post-launch—while collaborating closely with Product Managers, Engineers, Designers, BI Analysts, and Product Analysts from other divisions.

What  you’ll bring:

A great candidate for this role is passionate about working in a fast-paced product environment and is eager to enhance our product offering while creating an exceptional betting experience. 

• 2+ years of product analytics experience in a consumer internet business, or within a high-velocity, high-growth product or business unit.
• Experience assessing the impact of A/B experiments in a fast-paced product development cycle, ideally using platforms such as Kameleoon or Optimizely.
• Proficiency in SQL for data manipulation.
• Ability to independently determine and establish data analysis priorities, balancing ongoing projects with requests from various stakeholders.
• Experience with GA4 and other product or experience analytics platforms, with strong skills in creating dashboards in Looker Studio or similar visualization tools.
• Strong written communication skills to share insights and influence stakeholders.
• Detail-oriented, impact-driven, customer-centric approach to data analysis.

Nice to have:

• Previous experience in the sports betting industry.
• Experience with BI/visualization tools such as PowerBI.
• Familiarity with Databricks or similar big data platforms.

Kaizen Gaming Perks

• 🕑 Hybrid way of working;
• 🏃 A buddy will support you with your onboarding;
• 💸 Competitive pay & bonus scheme;
• ⭐Developmental 360° feedback framework;
• 💰 Monthly meal allowance;
• 👩‍⚕️ Private health insurance for you and your family;
• 📚 Unlimited access to Udemy & continuous training;
• 👨‍👩‍👧‍👦 Family Support.
• #LI-Hybrid
• #LI-GP2

Let's start with the role

As a Senior Data Scientist, you will be at the forefront of our AI developments,  analyzing data, building machine learning models to support our AI products and working along with the rest of the tech team to create successful AI products. The ideal candidate will have a deep understanding of machine learning algorithms and proven extensive experience in building  ML/AI powered applications in production.

As a Senior Data Scientist you will:

• Translate product requirements to machine learning problems and  identifying areas where AI can have the most business impact;
• Analyze data to identify significant patterns and generate actionable insights;
• Perform exploratory data analysis EDA and feature engineering to support the modeling process;
• Apply best practices on model selection, parameter tuning etc.;
• Run comparative experiments for model training;
• Analyze ML metrics to evaluate different potential solutions;
• Manage the full lifecycle of AI features, from data collection to model design and to implementation and optimization in production environments;
• Mentor and guide junior members, sharing knowledge and leading complex projects. 

What you’ll bring:

Must have:

• Expert knowledge of machine learning algorithm and respective theory;
• Extensive hands-on experience in delivering machine learning models to production;
• Experience in developing MLOps solutions;
• Knowledge of the Python machine learning ecosystem and solid software background in OOP;
• Knowledge of Deep learning;
• Strong skills in teamwork, communication, and analytical thinking;
• Fluency in English, both oral and written.

Nice to have:

• Experience with Big Data tools (Spark/PySpark) and cloud environment, ideally Azure / Databricks;
• Experience in recommendation systems;
• Experience in GenAI application development;
• Relevant industry experience i.e. Fraud detection, CRM, Sportsbook etc.

Kaizen Gaming Perks

• 🕑 Hybrid way of working & remote options
• 🏃 A buddy will support you with your onboarding
• 💸 Competitive pay and bonus scheme
• 👩‍⚕️ Private health insurance for you and your family
• 💰 Monthly meal allowance 
• ⭐Developmental 360° feedback framework
• 📚 Unlimited access to Udemy & continuous training

#LI-Remote

Let's start with the role

At Kaizen, our aim is to make data driven decisions in order to automate our servicers while also focusing on offering tailored customer experiences. Our machine learning team is dedicated towards this mission by building a variety of models, from binary classification tasks up to recommendation systems. We focus on transforming business needs into production applications and we cover a wide range of business sectors utilizing different data types and handling a broad project diversity. 

Our teams comprise of three different roles, data scientists, machine learning engineer and data engineers so that they include the full skillset to deliver projects to production.

We are currently looking for data scientists in all levels to join our teams. The role should combine strong knowledge of machine learning algorithms, analytical skills, experimental design etc. to support the model development process.

• Translate business requirements to machine learning problems;
• Perform EDA and feature engineering;
• Run comparative experiments for model training;
• Implement best practices on model selection, parameter tuning etc.

What you’ll bring

• A background in Computer Science, Statistics, Math or related field;
• Strong knowledge of machine learning algorithm and respective theory;
• 2-8 years of hands-on experience in delivering machine learning models to production;
• Knowledge of the Python machine learning ecosystem;
• Experience with Spark (PySpark);
• Solid software background in OOP;
• Strong skills in teamwork, communication, and analytical thinking;
• Fluency in English, both oral and written.

Nice to have

• Experience with Azure / Databricks;
• Knowledge of Deep learning;
• Experience in recommendation systems.

#Artificial Intelligence (AI) #Machine Learning (ML) #Data Science (DS)

Kaizen Gaming Perks

• 🕑 Hybrid way of working
• 🏃 A buddy will support you with your onboarding
• 💸 Competitive pay & bonus scheme
• ⭐Developmental 360° feedback framework
• 💰 Monthly meal allowance
• 👩‍⚕️ Private health insurance for you and your family
• 📚 Unlimited access to Udemy & continuous training
• 👨‍👩‍👧‍👦 Family Support.
• #LI-Remote
• #LI-PROMOTE

Are you passionate about working with the most cutting-edge technologies, including AI, big data analytics, LLMs, data mining, and business intelligence (BI)? Are you looking to become part of a world-class engineering team? Do you aspire to contribute to a globally recognized, pre-IPO leader in technology?

As a Senior Software Engineer, you will contribute to the design and development of AI-driven architectures that power our next-generation intelligent customer engagement platforms. Your contributions will help shape AI-powered customer experiences, enhancing the intelligence and efficiency of our service platforms.

Job Responsibilities:

• Develop and enhance a cloud-based data foundation system and AI agent platform. This involves leveraging AI/ML models, applying prompt engineering techniques, and conducting data analysis across structured, semi-structured, and unstructured data. Utilize technologies such as data and pattern mining, business intelligence (BI), and data transformation and processing.
• Collaborate with cross-functional teams, including data scientists, software engineers, product managers, and business stakeholders, to deliver a high-performance, reliable, scalable service with assured AI quality.
• Contribute to architectural discussions, providing technical design input and ensuring alignment with the overall technology roadmap while adopting best practices effectively and independently.
• Participate in shaping the engineering roadmap, ensuring solutions are scalable and extensible while meeting business needs.
• Support and mentor junior engineers, fostering a collaborative and innovative development culture.
• Stay up-to-date with industry trends, emerging technologies, and advancements in AI and data engineering.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• 5+ years of experience in backend development for cloud-based services, with a strong focus on scalability, reliability, and performance.
• Proficiency in Java, Python, C++, C#, Kotlin, Scala, or similar languages.
• Experience building and optimizing data-intensive services, including data processing, transformation, storage, and retrieval at scale.
• Strong problem-solving skills and ability to design scalable solutions for AI-driven services.
• Strong written and verbal English communication skills.
• Prior experience with data platforms and/or AI services is highly desirable.
• Experience working with distributed data platforms and modern data architectures, such as Apache Spark, Databricks, Kafka, Delta Lake, or similar is a desirable.
• Experience in data science and practical experience applying AI/ML models is a plus.

Additional Notes:
This position will follow a hybrid work model.

As a Machine Learning Platform Engineer at Iterable, you will design, evolve, and maintain our core ML infrastructure and the paved roads on which we build intelligence into our product. As a high tech marketing platform, we work with thousands of high scale and diverse enterprise customers. The scale and diversity of these workloads, supporting personalization with deep customizability, presents unique and exciting challenges for platform engineering. 

One of our core values is a growth mindset and Iterable is a company where everyone can grow. If this is a role that excites you, please do apply as we value applicants for the skills they bring beyond a job description.

What You’ll Do

• Lead the end-to-end development of ML pipelines, from prototyping and experimentation through to production deployment, enabling AI-powered features at scale.
• Design, build, and maintain the core infrastructure that powers our ML services (e.g., Databricks, AWS, Ray, Kubernetes).
• Develop robust observability and experimentation frameworks to ensure reliable model deployment, monitoring, and continuous iteration.
• Partner closely with Data Science teams, treating the ML Platform as a product—removing infrastructure bottlenecks and empowering them to deliver high-quality models efficiently and confidently.

What We’re Looking For

• 3+ years of experience in backend and/or infrastructure engineering.
• Strong experience with infrastructure-as-code practices (e.g., Pulumi, Terraform, CloudFormation).
• Experience working with service-oriented architectures and integrating ML services into high-scale backend systems.

Nice to Have

• Hands-on experience building and operating ML pipelines, including traditional recommender systems and modern deep learning training and serving systems.

What we offer 

• Competitive salaries & meaningful equity
• Private Medical Insurance
• Life/Risk Assurance
• Meal Allowance: 8.55€ per day
• Community Days (days for us to give back to the community) 
• Paid Annual Leave (22 days)
• Global Lifestyle Reimbursement Account
• Paid Sabbatical
• Complete laptop workstation

The Opportunity

As a Staff Data Scientist at PandaDoc, you will serve as a senior analytical leader, embedding yourself deeply in our product and business data to uncover non-obvious insights and drive actionable recommendations. A primary focus of this strategic role is to champion and drive the organizational shift toward a data-driven culture. You will own the advancement of our experimentation capabilities, train other analysts and data scientists on causal methodologies, and leverage your expertise to provide leadership with a clear, reliable understanding of true impact and causality.

You will report to the Director of GTM Data and act as a strategic thought partner to Go-to-Market teams, Marketing, Product, Finance, Design, Engineering, and executive leadership, ensuring alignment between data insights and critical business decisions.

What You'll Do

Experimentation & Causal Strategy

• Lead the Experimentation Roadmap: Define, champion, and execute a strategic roadmap for measuring impact across PandaDoc, focusing on high-leverage business questions related to customer workflows, churn risk, and long-term value (LTV).
• Advanced Experiment Design: Design, implement, and rigorously analyze complex A/B tests, multivariate experiments, and adaptive experimentation methods, including the application of Bayesian experimentation, to assess the effectiveness of proposed product changes and business levers.
• Causal Inference Beyond A/B: Apply advanced causal inference techniques (e.g., difference-in-differences, synthetic control, propensity score matching, and instrumental variables) to scenarios where randomized controlled trials (RCTs) are infeasible.
• Deep Dive Analysis: Conduct complex, proactive, and exploratory analysis to discover latent user behavior, emerging trends, and root causes of changes in key metrics, translating these findings into actionable product and business insights.
• Develop Measurement Frameworks: Define, instrument, and govern a unified Key Performance Indicator (KPI) framework that maps low-level product health metrics to high-level business outcomes, ensuring consistent and scalable measurement across the organization.

Technical Leadership & Influence

• Scaling Data Science: Partner with Data Engineering to design and build scalable, self-serve experimentation tooling and reusable analytical assets and frameworks (e.g., causal machine learning models) that empower other analysts and data consumers.
• Strategic Influence: Act as a strategic thinker by translating complex statistical findings into clear, compelling, and actionable business narratives for cross-functional partners and senior leadership (VP/C-suite), driving strategic decisions and investment priorities.
• Mentorship and Training: Serve as a technical subject matter expert, training and mentoring junior and mid-level data scientists on best practices in statistical rigor, experimental design, and causal modeling.

About You

Qualifications

• Experience: 6+ years of professional experience in an applied data science, economics, or product analytics role, with a proven track record of leveraging experimentation and causal inference methods to drive significant business impact.
• Education: B.A. or B.S. in Mathematics, Statistics, Economics, Computer Science, or a related quantitative discipline. A Master’s degree in a quantitative field (e.g., Statistics, Data Science, Econometrics, Operations Research) is preferred, but not required.

Required Technical Expertise

• Causal Inference: Demonstrated expertise in applying a wide range of Causal Inference methods, e.g. Quasi-Experimentation, Matching Methods (PSM), Difference-in-Differences, and/or Instrumental Variables.
• Experimentation Methodologies: Expertise in advanced statistical methodologies for A/B testing, including sample size calculations, sequential testing, dealing with interference/network effects, variance reduction techniques (e.g., CUPED), etc.
• Deep Analytical Methods: Mastery of advanced statistical modeling, time-series analysis, and quantitative methods necessary to perform thorough exploratory data analysis, produce timely insights, and provide actionable recommendations.
• Programming: Advanced proficiency in Python or R for statistical modeling, with experience using relevant data science packages (e.g., SciKit-Learn, numpy, pandas).
• Data Tools: Expert-level proficiency in SQL and experience working with established data warehouses (e.g., Snowflake, Postgres).
• Data Pipelining: Experience with data transformation and workflow management tools such as dbt, Airflow, or Databricks is a strong plus.

Key Attributes

• Strategic Communication & Influence: Possesses exceptional communication, presentation, and data storytelling skills with a consistent record of influencing cross-functional partners and leadership at all levels, particularly in navigating and driving consensus in unstructured or ambiguous environments.
• Change Management: Proven ability to drive organizational change management in environments where experimentation and data-driven decision-making are not yet widely adopted.
• Thrive in ambiguity: Ability to navigate significant ambiguity, translate complex business questions into clear analytical frameworks, and manage multiple competing priorities in a fast-paced environment.
• Relevant Experience: Experience in a SaaS domain and a strong focus on Product Data Science are strongly preferred.

Company Culture: 

• We're known for our work-life balance, kind co-workers, & creative virtual team-bonding events. And although our Pandas are located across the globe, we stay connected with the help of technology and ensure that everyone on our team feels, well, like a team.
• Pandas work best when they're happy. We retain our talent by upholding our values of integrity & transparency, and selling a product that changes the lives of our customers. 
• Check out our LinkedIn to learn more. 

Benefits:

• Our benefits include tremendous career growth opportunities, a competitive salary, health and commuter benefits, company paid life & disability, 20+ PTO days, 401K and FSA plans, and of course, a fun team of Pandas to work with!

PandaDoc is an Equal Opportunity Employer. We are committed to equal treatment of all employees without regard to race, national origin, religion, gender, age, sexual orientation, veteran status, physical or mental disability or other basis protected by law.

The Opportunity

As a Staff Data Scientist at PandaDoc, you will serve as a senior analytical leader, embedding yourself deeply in our product and business data to uncover non-obvious insights and drive actionable recommendations. A primary focus of this strategic role is to champion and drive the organizational shift toward a data-driven culture. You will own the advancement of our experimentation capabilities, train other analysts and data scientists on causal methodologies, and leverage your expertise to provide leadership with a clear, reliable understanding of true impact and causality.

You will report to the Director of Product Data and act as a strategic thought partner to Product, Finance, Design, Engineering, Product Marketing, and executive leadership, ensuring alignment between data insights and critical business decisions.

What You'll Do

Experimentation & Causal Strategy

• Lead the Experimentation Roadmap: Define, champion, and execute a strategic roadmap for measuring impact across PandaDoc, focusing on high-leverage business questions related to customer workflows, churn risk, and long-term value (LTV).
• Advanced Experiment Design: Design, implement, and rigorously analyze complex A/B tests, multivariate experiments, and adaptive experimentation methods, including the application of Bayesian experimentation, to assess the effectiveness of proposed product changes and business levers.
• Causal Inference Beyond A/B: Apply advanced causal inference techniques (e.g., difference-in-differences, synthetic control, propensity score matching, and instrumental variables) to scenarios where randomized controlled trials (RCTs) are infeasible.
• Deep Dive Analysis: Conduct complex, proactive, and exploratory analysis to discover latent user behavior, emerging trends, and root causes of changes in key metrics, translating these findings into actionable product and business insights.
• Develop Measurement Frameworks: Define, instrument, and govern a unified Key Performance Indicator (KPI) framework that maps low-level product health metrics to high-level business outcomes, ensuring consistent and scalable measurement across the organization.

Technical Leadership & Influence

• Scaling Data Science: Partner with Data Engineering to design and build scalable, self-serve experimentation tooling and reusable analytical assets and frameworks (e.g., causal machine learning models) that empower other analysts and data consumers.
• Strategic Influence: Act as a strategic thinker by translating complex statistical findings into clear, compelling, and actionable business narratives for cross-functional partners and senior leadership (VP/C-suite), driving strategic decisions and investment priorities.
• Mentorship and Training: Serve as a technical subject matter expert, training and mentoring junior and mid-level data scientists on best practices in statistical rigor, experimental design, and causal modeling.

About You

Qualifications

• Experience: 6+ years of professional experience in an applied data science, economics, or product analytics role, with a proven track record of leveraging experimentation and causal inference methods to drive significant business impact.
• Education: B.A. or B.S. in Mathematics, Statistics, Economics, Computer Science, or a related quantitative discipline. A Master’s degree in a quantitative field (e.g., Statistics, Data Science, Econometrics, Operations Research) is preferred, but not required.

Required Technical Expertise

• Causal Inference: Demonstrated expertise in applying a wide range of Causal Inference methods, e.g. Quasi-Experimentation, Matching Methods (PSM), Difference-in-Differences, and/or Instrumental Variables.
• Experimentation Methodologies: Expertise in advanced statistical methodologies for A/B testing, including sample size calculations, sequential testing, dealing with interference/network effects, variance reduction techniques (e.g., CUPED), etc.
• Deep Analytical Methods: Mastery of advanced statistical modeling, time-series analysis, and quantitative methods necessary to perform thorough exploratory data analysis, produce timely insights, and provide actionable recommendations.
• Programming: Advanced proficiency in Python or R for statistical modeling, with experience using relevant data science packages (e.g., SciKit-Learn, numpy, pandas).
• Data Tools: Expert-level proficiency in SQL and experience working with established data warehouses (e.g., Snowflake, Postgres).
• Data Pipelining: Experience with data transformation and workflow management tools such as dbt, Airflow, or Databricks is a strong plus.

Key Attributes

• Strategic Communication & Influence: Possesses exceptional communication, presentation, and data storytelling skills with a consistent record of influencing cross-functional partners and leadership at all levels, particularly in navigating and driving consensus in unstructured or ambiguous environments.
• Change Management: Proven ability to drive organizational change management in environments where experimentation and data-driven decision-making are not yet widely adopted.
• Thrive in ambiguity: Ability to navigate significant ambiguity, translate complex business questions into clear analytical frameworks, and manage multiple competing priorities in a fast-paced environment.
• Relevant Experience: Experience in a SaaS domain and a strong focus on Product Data Science are strongly preferred.

Company Culture: 

• We're known for our work-life balance, kind co-workers, & creative virtual team-bonding events. And although our Pandas are located across the globe, we stay connected with the help of technology and ensure that everyone on our team feels, well, like a team.
• Pandas work best when they're happy. We retain our talent by upholding our values of integrity & transparency, and selling a product that changes the lives of our customers. 
• Check out our LinkedIn to learn more. 

Benefits:

• Our benefits include tremendous career growth opportunities, a competitive salary, health and commuter benefits, company paid life & disability, 20+ PTO days, 401K and FSA plans, and of course, a fun team of Pandas to work with!

PandaDoc is an Equal Opportunity Employer. We are committed to equal treatment of all employees without regard to race, national origin, religion, gender, age, sexual orientation, veteran status, physical or mental disability or other basis protected by law.

Our Data & Engineering team is growing, and we’re now looking for a Senior Data Consultant (m/f/*) to join our team in Portugal, supporting our global growth.

As a Senior Data Consultant, you will combine deep hands-on experience in modern data platforms — particularly Databricks — with a strategic, client-oriented mindset. You’ll advise organizations on how to design, implement, and scale data-driven architectures, while guiding them through every stage of their digital transformation journey.

WHAT WE OFFER

• Flexible work options, including fully remote or hybrid arrangements (candidates must be located in Portugal)
• The chance to accelerate your career and work with outstanding colleagues in a collaborative, global learning community across three continents
• Opportunities to shape cutting-edge data projects and see your ideas implemented in real-world solutions
• Balance your work and personal life: choose to work from home, in the office, or in a hybrid setup
• Annual performance reviews and continuous feedback focused on collaboration and growth, not hierarchy
• A personalized development plan and professional advancement opportunities
• Access to learning resources — paid certifications, unlimited Udemy Business, and more
• Local, virtual, and global team events to connect with colleagues worldwide

WHAT YOU’LL DO

• Advise clients on the design and implementation of scalable data architectures using Databricks Lakehouse Platform, with a strong focus on Apache Spark, Delta Lake, and Unity Catalog
• Translate complex business requirements into data strategies, architecture blueprints, and technical roadmaps
• Design, implement, and optimize data pipelines for analytics, machine learning, and business intelligence workloads
• Collaborate with stakeholders — from executives to technical teams — to align data initiatives with business goals
• Ensure data quality, governance, and security best practices within Databricks and related ecosystems
• Support pre-sales and discovery activities by identifying client needs and defining technical approaches
• Mentor junior consultants and contribute to the growth of our Databricks and data consulting expertise
• Stay current with emerging technologies and trends in cloud data platforms and AI-driven analytics

WHAT YOU’LL BRING

• Proven experience in data consulting or architecture, with hands-on expertise in Databricks on Azure, AWS, or GCP
• Deep knowledge of Spark (PySpark/Scala), Delta Lake, and MLflow
• Strong programming skills in Python or Scala, and experience with CI/CD pipelines (GitHub Actions, Azure DevOps, etc.)
• Solid understanding of data modeling, distributed computing, and performance optimization in cloud environments
• Experience advising or working directly with enterprise clients on data modernization or cloud migration projects
• Familiarity with orchestration tools (e.g., Databricks Workflows, Airflow) and infrastructure-as-code (e.g., Terraform)
• Excellent communication and stakeholder management skills, with the ability to translate technical solutions into business value
• Professional proficiency in English (C1 level, written and spoken)

Did we pique your interest, or do you have any questions?
We’d love to hear from you — contact us at recruiting@ultratendency.com

Our Engineering community is growing, and we’re now looking for a Senior Data Engineer - Databricks (m/f/*) to join our team in Portugal, supporting our global growth.

WHAT YOU’LL DO