About us

The Tripadvisor Group connects people to experiences worth sharing, and aims to be the world’s most trusted source for travel and experiences. We leverage our brands, technology, and capabilities to connect our global audience with partners through rich content, travel guidance, and two-sided marketplaces for experiences, accommodations, restaurants, and other travel categories. The subsidiaries of Tripadvisor, Inc. (Nasdaq: TRIP), include a portfolio of travel brands and businesses, including Tripadvisor, Viator, and TheFork.

We strive to create an accessible and inclusive experience for all candidates. If you need a reasonable accommodation during the application or the recruiting process, please make sure to reach out to your individual recruiter or our team at AccessibleRecruiting@tripadvisor.com.

If you have any additional questions about careers at Tripadvisor you can email us at recruitment@tripadvisor.com. We have all the answers!

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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.

Upwork Inc.’s (Nasdaq: UPWK) family of companies connects businesses with global, AI-enabled talent across every contingent work type including freelance, fractional, and payrolled. This portfolio includes the Upwork Marketplace, which connects businesses with on-demand access to highly skilled talent across the globe, and Lifted, which provides a purpose-built solution for enterprise organizations to source, contract, manage, and pay talent across the full spectrum of contingent work. From Fortune 100 enterprises to entrepreneurs, businesses rely on Upwork Inc. to find and hire expert talent, leverage AI-powered work solutions, and drive business transformation. With access to professionals spanning more than 10,000 skills across AI & machine learning, software development, sales & marketing, customer support, finance & accounting, and more, the Upwork family of companies enables businesses of all sizes to scale, innovate, and transform their workforces for the age of AI and beyond.

Since its founding, Upwork Inc. has facilitated more than $30 billion in total transactions and services as it fulfills its purpose to create opportunity in every era of work. Learn more about the Upwork Marketplace at Upwork.com and follow us on LinkedIn, Facebook, Instagram, TikTok, and X; and learn more about Lifted at Go-Lifted and follow on LinkedIn.

As a Lead AI Engineer on our Data Platform & Services (DPS) team, you will apply your expertise in AI capabilities to build and deploy production grade AI powered systems towards data engineering, analytics & product evolution use cases.  You’ll design and deploy agentic frameworks, natural language querying (NLQ) capabilities, and prompt/LLM evaluation strategies that unlock insights at scale across Upwork. This is an opportunity to deliver tangible impact, enabling faster decision-making, democratizing data access, and empowering analysts, product teams and stakeholders throughout the company.

Responsibilities

• Design and implement scalable agentic frameworks that coordinate multiple AI agents to deliver accurate and reliable outcomes.
• Build and optimize natural language query solutions that provide compliant, intuitive, and self-service access to enterprise data.
• Develop and maintain evaluation frameworks for LLMs and prompts, driving measurable improvements in accuracy, scalability, and resilience.
• Partner with data engineering peers to integrate AI orchestration with Python, SQL, and Snowflake pipelines.
• Contribute to AI-powered summarization and explanation tools that enhance reporting, insights, and executive reviews.
• Collaborate cross-functionally with product, analytics, and business teams to ensure solutions meet measurable business goals.

What it takes to catch our eye

• Proven experience delivering AI-powered data systems in production, including hands-on expertise with LLMs and prompt engineering.
• Strong foundation in modern data engineering, with proficiency in Python, SQL, and platforms such as Snowflake.
• Demonstrated ability to design agentic orchestration frameworks that scale across diverse business scenarios.
• A collaborative problem-solver with strong communication skills, able to align stakeholders and create clarity across teams.
• Growth mindset, excited to learn, iterate, and deliver measurable business outcomes through applied AI.

Come change how the world works.

About DataCamp

DataCamp's mission is to empower everyone with the data and AI skills essential for 21st-century success. By providing practical, engaging learning experiences, DataCamp equips learners and organizations of all sizes to harness the power of data and AI. As a trusted partner to over 17 million learners and 6,000+ companies, including 80% of the Fortune 1000, DataCamp is leading the charge in addressing the critical data and AI skills shortage.

About the role

This role is suitable for experienced Data Engineers with 3 years plus of experience, so only apply for this role if your experience matches that criteria. This is a Data Platform Engineering role sitting within our Platform Engineering department, alongside our Infrastructure and Dev Platforms teams. You will be building the data platform that the rest of DataCamp depends on, not doing analytics work for a single business unit.

DataCamp, being a data-driven organization, runs a Datalakehouse on Google Cloud's BigQuery and our reporting is created using our Looker BI tooling. This BI reporting capability helps the DataCamp leadership team steer the company OKRs and vision, and it also helps our engineering teams make data-driven decisions on everything we do. Data also helps us measure how successful our product features or initiatives have been.

To provide data analytics for all our staff, DataCamp has a completely automated data pipeline built using an "infrastructure as code" methodology with Terraform and Ansible, which allows infrastructure provisioning of all data engineering tooling. DataCamp operates a data pipeline that ingests data using Airbyte, and our Airflow cluster schedules a series of tasks each day to transform and clean our data and seed our data marts that are authored using DBT, which helps simplify reporting for each of our business units.

DataCamp currently has data marts for finance, engineering KPIs, engineering costs, and infrastructure cost, to make reporting as easy as possible for our stakeholders in those business units, and we aim to extend that offering. DataCamp's leadership team, data scientists, data analysts and engineers all require the latest datasets to be refreshed on a daily basis so they can do their daily tasks. It is expected that the data platform engineering team makes sure there are no issues with the daily refresh of data and also provides consultancy on best practices such as data ingestion, data mart design, or data product design.

A large part of this role is enabling AI agents and MCPs across the company. We are in a strong agent-first engineering push at DataCamp. That means the data platform is no longer just serving humans through Looker dashboards, it is serving Claude, Claude Code and other agents through MCP servers that expose BigQuery, dbt metadata, data marts and internal tooling in a safe, governed way. You will be building and maintaining those MCPs, shaping how teams across DataCamp get data into their agent workflows, and setting the standards for how agent-authored changes land in our data platform.

What to expect from DataCamp if you join us as a Data Engineer

It will be your role, as part of the Platform Engineering department, to work directly with our staff data engineer, the wider platform engineering teams, our data science and analytics teams, and stakeholders across product and engineering on all data initiatives, both internally and as part of DataCamp's data products.

You should join us at DataCamp if you want to be fully supported in learning how to create and maintain best-in-class data platform engineering processes in an agent-first environment. If you enjoy learning in a team environment, our data and platform experts will be on hand to provide a support network to help you in your career development. You will also be empowered to have creative freedom to shape the processes and roadmap for data platform engineering at DataCamp, including how we expose data to agents, how we govern agent access to production data, and how we measure the impact of agent-assisted data work.

You will spend a meaningful portion of your time working with and through AI agents, writing AGENTS.md specifications, building MCP connectors, designing skills, and using Claude Code day to day to move faster on pipeline work, dbt changes, and operational tasks. If all of this sounds exciting, then this could be the ideal job for you. We don't expect you to know everything when you join, but we want you to be as passionate about data and agent workflows as we are. What we expect in return is that you are naturally inquisitive, open to constructive feedback, and willing to learn and grow every day.

This role should give you a collaborative working environment to become a world-class data platform engineer and help you in your career. Our aim is for you to build the skills needed to consult and advise on data mart design, data product design, MCP architecture, and how to leverage AI agents to improve our internal processes. You will also be involved in helping our content team provide data engineering courses on Redshift, BigQuery, Snowflake, and all the different widely used data warehousing tooling. This may sound like a lot, and it is, but within our Platform Engineering department we are confident we can support you every step of the way and we can achieve these outcomes as a team.

What do we expect from you as a Data Engineer

DataCamp has a strong bias towards providing self-serve systems for our teams so they can access the data they need to make data-driven decisions. That bias now extends to agents, where teams should be able to reach data through MCPs without us becoming a bottleneck. This means the data pipeline, MCP surfaces, and data development process need to be available, governed, and functional rather than a central bottleneck in the company.

You will, under the guidance of our staff data engineer, play a key part in planning future improvements for our whole data platform: the pipeline itself, the data development process, the testing process for data changes, and the agent and MCP surfaces that sit on top of it.

We need you to be enthusiastic and proactive by owning your day-to-day work or any operational issues that occur. We aim to do 80 percent capability development via planned quarterly OKR work and 20 percent support work. This work balance can only be achieved by automating everything we do and by leaning hard on agents to remove toil. We believe Data Platform Engineering should be a force multiplier for the business — both by giving humans self-serve data and by giving every team a reliable agent-accessible path to the data they need.

To be successful in the role it is essential you know Python and can write and advise on SQL best practices. Having an understanding of data ingestion, data processing, reverse ETL, a passion for data governance, and acting as a gatekeeper for security on our data platform is also a must. Comfort working with and through AI agents, not just using them to autocomplete, but designing workflows around them is also essential.

The ideal candidate

• Is an experienced Data Engineer of 3 years plus.
• Has experience of data warehousing (e.g. BigQuery or Snowflake) and data engineering related tools (e.g. Airflow, Looker, Metabase, Fivetran, Airbyte).
• Has knowledge of cloud platforms (AWS, GCP or Azure).
• Can develop in Python and write SQL.
• Has hands-on experience using AI coding agents (Claude Code, Cursor, or similar) as part of their day-to-day engineering workflow.
• Understands what MCP is, or is willing to learn it fast, and can reason about how to safely expose data and tooling to agents.
• Understands Scrum and Kanban.
• Has excellent oral and written communication skills, including the ability to write clear specifications for agents (AGENTS.md, skills, prompts).
• Is interested in understanding and scaling complex data pipelines.
• Is interested in monitoring and self-healing systems.
• Is highly organized with a flexible, can-do attitude and a willingness and aptitude for learning.
• Improves the team with code reviews, technical discussions, and documentation — including reviewing agent-authored PRs with a critical eye.
• Is able to work collaboratively in teams and develop meaningful relationships to achieve common goals.

It's a plus if

• You have experience in dbt.
• You have experience in Docker, CI/CD, Kubernetes.
• You have experience of data marts.
• You have experience with Infrastructure as Code (Terraform, Ansible, etc.).
• You have built or contributed to an MCP server, or integrated one into an agent workflow.
• You have experience with data catalog and governance tooling exposed through agent interfaces.
• You are passionate about data science, education, and applying AI agents to real engineering problems.

Why Datacamp? 

Joining DataCamp means becoming part of a dynamic, creative, and international start-up. Here are just a few of the reasons why you’ll love being on our team:

• Exciting challenges: Face new technical challenges daily, keeping your work engaging and rewarding.
• Competitive compensation: We offer a competitive salary with attractive benefits.
• Flexibility: Benefit from flexible working hours because the future is flexible! 
• Continuous learning: Access a yearly learning budget for conferences & training to support your professional growth.
• Global retreats: Participate in international company retreats, fostering a global team spirit.
• Equipment: Yearly refreshment of your IT Equipment budget for your home working setup.
• Amazing team: Collaborate with a truly exceptional team—seriously, we’re awesome!

Our competitive compensation package offers additional benefits. On top of your salary you will also receive extra legal benefits such as best-in-class medical insurance including dental and vision. Depending on your location additional benefits might be available to you.

At DataCamp, we value diverse experiences and perspectives. If you’re excited about this role but don't meet every qualification, we still encourage you to apply. We believe skills can be developed and are committed to fostering an inclusive workplace where everyone can thrive. Your unique talents and perspectives are what make our team great!

Scopely is looking for a Senior Director, Data & Insights  to help power the continued success of Monopoly GO! This opportunity is open to candidates working remotely across the UK, Ireland, or Portugal, or in a hybrid setup from our Barcelona or Seville hubs.

At Scopely, we care deeply about what we do and want to inspire play every day - whether in our work environments alongside our talented colleagues or through our deep connections with our communities of players. We are a global team of game lovers who are developing, publishing and innovating the mobile games industry, connecting millions of people around the world daily.      

Monopoly GO! is a global phenomenon and the biggest mobile game launch in history, captivating millions of players worldwide. The team is based in Europe and the US, and works every day to create captivating new experiences for our players.     

As Sr. Director of Data & Insights, you'll lead three teams — Analytics Engineering, Data Science, and Product Analytics — each owned by a Director or Lead reporting to you. Your job is to operate above the day-to-day: set strategy, drive cross-functional alignment, and deliver a step-change in how the studio uses data.

Your north stars: speed (time-to-insight in minutes and hours, not days), access (every PM, designer, and marketer answers their own data questions without a ticket queue), and impact (translating data insight into actionable software improvements) 

AI-Forward Mandate

You'll set the AI vision and hold your organization accountable for embedding it across the org. We expect this leader to build the future of insight delivery, not just manage the present.

What You'll Own

You are the ultimate craft leader for Data & Insight and drive impact through the following areas: 

Analytics Engineering — via Director/Lead

• Define the vision and operating model for Analytics Engineering as the company scales
• Set the direction for the data platform (pipelines, warehouse, semantic layer), focusing on speed, reliability, and ease of use
• Drive the adoption of a data product mindset across all areas
• Push the shift toward AI-enabled workflows in analytics, both for how data is built and how it’s used
• Define how AI systems interact with our data, enabling simple, self-serve access for both technical and non-technical users
• Own the principles behind the metrics layer as a single source of truth across the company
• Maintain a strategic relationship with the central Scopely data function 

Data Science — via Director/Lead

• Set direction and provide feedback for the Data Science roadmap 
• Identify and bridge the gap in missing Data Science models and frameworks 
• Set expectations for how AI should enhance decision-making and operational workflows, and increase the ceiling is what’s possible with Data Science 

Product Analytics — via Director/Lead

• Align Product Analytics direction and roadmap with product strategy, ensuring insights directly inform roadmap decisions and prioritization
• Define the Product Analytics operating model (team structure, embedding, ways of working) to maximize impact and enable proactive, high-quality decision-making
• Set expectations for AI-enabled analytics, improving insight generation, scalability, and team productivity while maintaining rigor

Leadership Scope

• Directly manage 3 Director/Lead-level reports; indirectly lead 40+ analysts, engineers, and data scientists
• Coach and develop your direct reports
• Own the data roadmap and OKRs; serve as the data voice at the product executive level
• Build data literacy programs so non-technical partners can self-serve confidently

What We're Looking For

• 10+ years in data, analytics, or data science; 5+ years managing director-level leaders
• Proven track record in live-service mobile games or high-scale consumer apps
• Deep expertise in the modern data stack: dbt, Snowflake/BigQuery, Looker/Tableau/Hex
• Hands-on fluency with LLM patterns (RAG, agents, prompt engineering) and AI tooling deployment
• Strong command of experimentation design, causal inference, and ML systems in production
• Leads through delegation — comfortable owning strategy and outcomes without owning execution
• Exceptional communicator from SQL to C-suite; player-first and outcome-first mindset

Please ensure that the résumé/CV you attach is written in English.

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.

We're seeking an experienced Engineering Project Manager to join our team in Portugal. 

✅ Requirements:

✔️ At least 4 years of experience in IT;

✔️ Excellent knowledge of Agile methodologies, Scrum & Kanban frameworks is a must;

✔️ Knowledge of Software Development Life Cycle (SDLC);

✔️ Managing team experience;

✔️ Knowledge of Jira, Confluence, Miro, OKRs;

✔️ Advanced verbal communication skills;

✔️ Knowledge of time management;

✔️ Be a decision maker and possess problem-solving skills;

✔️ Proven ability to work at multiple tasks at a time, meet deadlines, prioritize tasks;

✔️ Upper-Intermediate spoken and written English.

✅ It would be a big plus if the person is at least familiar with, or has worked around, the following technologies:

Cloud & Infrastructure: AWS, Docker, Kubernetes, Cloudflare

Data: ClickHouse, MySQL, Snowflake, MongoDB, Redis

Messaging/Queues: Kafka, NATS, BullMQ

CI/CD & Monitoring: GitHub Actions, Jenkins, Grafana, Prometheus, ELK Stack

✅ Nice to have:

✔️ PMP, PSM or the equivalent certificates.

✅ Responsibilities:

✔️ Managing, monitoring, and forecasting project execution;

✔️ Time and efforts tracking, regular status reporting;

✔️ Team coordination;

✔️ Project risk management;

✔️ Maintaining project documentation.

✅ We offer excellent benefits, including but not limited to:

 💻Learning and development opportunities and interesting challenging tasks;

✈️ Relocation package (tickets, staying in a hotel for 2 weeks and visa relocation support for our employees and their family members);

🏥 Private medical coverage including inpatient, outpatient, dental care, annual check-ups, maternity support.

🎾 Partial compensation for tennis and padel lessons;

🏋️ Urban Sport membership benefit (the most diverse sports and wellness offering in Europe with more than 50+ activities)

📚 Opportunity to develop language skills and partial compensation for the cost of English and Portuguese language classes (for localization purposes).

🎁 Birthday celebration present;

🏝 Time for proper rest, with 24 non-business days per year and additional 6 paid sick days.

🚗 Transport compensation - 300 euros net per month.

⚖️ Competitive remuneration level with annual review.

🏄🏻  Teambuilding activities.

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.