Data Scientist

Takeaway.com; Amsterdam; On-site; EUR 70,000 - 110,000; Full time

Job summary

Just Eat Takeaway.com in Amsterdam is seeking a Data Scientist to join the User Behaviour Analytics team. You will own machine learning products that predict customer behaviour and directly inform marketing decisions across the marketplace.

You will manage the full ML lifecycle, from data analysis and model development through deployment to cloud-based ML infrastructure, with ongoing monitoring to ensure reliable, scalable outputs. Collaboration with Data, ML Engineers, and Marketing is key.

Qualifications

• Hands-on ML experience building and validating models.
• Production-grade Python coding and data pipelines.
• Experience deploying models to cloud infrastructure.
• Ability to translate complex results to non-technical stakeholders.
• Strong collaboration with Data, ML Engineers, and Marketing.

Responsibilities

• Lead ML lifecycle from data analysis to production monitoring.
• Develop models predicting customer behaviour for marketing decisions.
• Write production-grade Python and SQL pipelines for scalable ML.
• Monitor and improve model performance to reduce degradation.
• Collaborate with Data, ML Engineers, and Marketing teams.

Skills

Practical ML

ML techniques fluency

Production-grade Python

Proficient SQL

AI-assisted workflows

Git version control

Communication

Autonomy

Collaboration

Tools

DBT

Airflow

Dagster

Vertex AI

BigQuery

AWS

Job description

Ready for a challenge?

Then Just Eat Takeaway.com might be the place for you. We’re a leading global online delivery platform, and our vision is to empower everyday convenience.

Whether it’s a Friday-night feast, a post-gym poke bowl, or grabbing some groceries, our tech platform connects tens of millions of customers with hundreds of thousands of restaurant, grocery and convenience partners across the globe.

• Location: Amsterdam Office
• Full time

About this role

In this role, you will join the User Behaviour Analytics (UBA) team, the group responsible for building the data foundations that power all customer experiences at Just Eat Takeaway. We work collaboratively across CRM, Product, and Marketing to lead with data, deliver trusted insights, and care about the quality and impact of what we build.

As a Data Scientist in the User Behaviourial Analytics team, you will own machine learning products that predict customer behaviour and directly inform marketing decisions across Just Eat Takeaway's marketplace. Your models will power use cases such as Customer Lifetime Value, Inactivity and Order Frequency Segment predictions, enabling campaign targeting and value-based bidding.

You will be responsible for the full ML lifecycle, from data analysis and model development through to productionisation and monitoring of predictive performance. This includes writing production-grade Python code and efficient SQL pipelines, deploying models to cloud-based ML infrastructure for both batch and near real-time predictions, and continuously monitoring model quality to ensure reliable, scalable outputs. You will also be involved in measuring model impact on business metrics through experiments, helping the team understand how predictions translate into tangible business performance.

These are some of the key components to the position

• Model Development & Assessment: Lead well-defined assignments to develop, maintain, and improve machine learning models that predict customer behaviour. Select appropriate techniques for each problem, validate rigorously, and track performance against business objectives.
• Data Analysis & Engineering: Use SQL and Python to acquire, transform, and clean large-scale datasets. Apply appropriate techniques for handling outliers, missing values, and exploratory data analysis to create data structures relevant to customer behaviour prediction problems.
• Production Standards & Model Monitoring: Write production-quality Python code and efficient SQL for data and ML model pipelines that can scale across our marketplace. Be mindful of production environment rules, deploy models to cloud-based infrastructure, and help monitor predictive performance of models in production — identifying and responding to degradation, or data quality issues.
• Process Improvement: Identify opportunities to automate or improve existing processes; recommend solutions based on established best practices.
• Actionable Insights: Translate analytics output into clear, actionable insights for decision-making. Assist in presenting these to stakeholders in easy-to-understand language, using data visualisation where appropriate.
• Cross-functional Collaboration: Work closely with Data, ML Engineers, and Marketing stakeholders; participate in cross-functional projects, building business acumen and domain knowledge from collaborating colleagues.
• Team Ways of Working: Contribute to sprint planning, technical prioritisation, and knowledge-sharing to help the team collaborate effectively and deliver together.

What will you bring to the team?

• Practical ML experience: hands-on experience building and validating ML models, comfortable working independently under general supervision while knowing when to seek guidance from more senior colleagues.
• ML technique fluency: familiarity with statistical and machine learning techniques (classification, regression, boosted trees, uplift modelling, forecasting, hyperparameter tuning) and how to select the right approach for a given problem.
• Production-grade Python: coding abilities that go beyond notebooks, writing clean, testable code suitable for engineering workflows and collaboration with technical teams.
• Proficient SQL: across complex data warehouses, including partitioning, window functions, and large-table joins, with focus on query efficiency and data quality.
• AI-assisted workflows: comfortable using AI tools and building skills to automate and accelerate day-to-day work, including writing, debugging, and optimising code, and analysing data.
• Version control: familiarity with Git for collaborative development and code management.
• Problem-framing and communication: translate ambiguous business questions into modelling approaches and explain technical results to non-technical stakeholders with respect and mutual support.
• Self-starter attitude: comfortable with autonomy and ambiguity, thriving in fast-paced environments where you can build rapidly, test rigorously, and roll out successes at scale.
• Strong collaboration skills: work effectively with senior data scientists, ML engineers, and Marketing teams across different levels and functions.

Nice to have

• Model productionisation experience: knowledge of deploying, versioning, and serving ML models in production environments.
• DBT and Airflow experience: Dbt for data transformation and Airflow / Dagster practice for pipeline development.
• Cloud platforms: hands-on experience with Vertex AI, BigQuery, and/or AWS for ML model training, deployment, and data processing.
• ML in Marketing: a strong interest in applying ML to marketing problems is a plus.

At JET, this is how we play

Our teams forge connections internally and work with some of the best-known brands on the planet, giving us truly international impact in a dynamic environment.

Fun, fast-paced and supportive, the JET culture is about movement, growth, helping one another to succeed and celebrating wins. By truly living our values and embodying our behaviours, we’re building a customer-first culture which enables us to stay one step ahead of the competition.