Senior Data Scientist

Takeaway.com

Senior Data Scientist building and owning ML solutions for JET's marketplace across multiple countries. Collaborating closely with ML and Data Engineers to impact customer journeys daily.

Posted 7/10/2026full-timeAmsterdam • 🇳🇱 NetherlandsSeniorWebsite

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

Own end-to-end ML projects from problem framing through to production deployment, defining approaches, managing trade-offs, and communicating findings to stakeholders
Produce technical design documents that guide modelling choices, weighing data availability, serving latency, interpretability, and long-term maintainability
Design and own LLM-based solutions for customer or partner representations, content personalisation, or recommendation, including training data curation and evaluation methodology
Build evaluation frameworks that link to business metrics, run well-powered online experiments, and investigate offline-to-online performance gaps
Close the gap between offline model performance and online business impact through rigorous experimentation and measurement
Act as primary technical reviewer for junior colleagues, providing substantive, teaching-oriented feedback that raises the team's overall standard
Collaborate with ML Engineers and Data Engineers to ensure models reach meaningful commercial scale across multiple markets
Shape technical direction across the AI stack, spanning search ranking, homepage personalisation, promotions intelligence, and foundation model applications

Requirements

What you’ll need

Several years of hands-on data science experience owning ML systems end-to-end in production environments
Production-quality Python skills and confident SQL for working with large-scale data warehouses
Experience building offline evaluation frameworks from scratch, including metric selection, holdout strategy, leakage checks, and documented links to business metrics
Practical experience designing data pipelines for production models, including feature specification, lookback window rationale, and handling sparse data and cold-start cases
Hands-on experience with retrieval, ranking, or embedding-based approaches in recommendation or personalisation contexts
Experience building evaluation frameworks for LLM-based outputs, including quality dimensions, judge pipelines, and hallucination checks
Confidence with A/B test design at scale, including randomisation unit selection, statistical power calculations, and accounting for novelty effects
Strong written and verbal communication skills, able to translate complex modelling decisions into clear recommendations for technical and non-technical audiences
An analytical problem-solving mindset, comfortable with ambiguity and able to bring clarity to complex questions
Familiarity with causal inference or uplift modelling in e-commerce or marketplace contexts (Bonus skills)
Parameter-efficient fine-tuning of large language models (Bonus skills)
Experience with ML orchestration or experiment tracking tooling (Bonus skills)

Benefits

Comp & perks

Flexible work arrangements
Professional development opportunities

ATS Keywords

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Hard Skills & Tools

Machine Learning SystemsPython ProgrammingSQL ProficiencyData Pipeline DesignEvaluation FrameworksRetrieval ApproachesRanking TechniquesEmbedding-Based ApproachesCausal InferenceParameter-Efficient Fine-Tuning

Soft Skills

Analytical Problem-SolvingStrong Communication Skills