Senior Data Scientist

The Role

We're looking for an experienced and impact-driven Senior Data Scientist to join our Discovery team, focused on helping customers find products they love through better search, recommendations, and personalisation.

This is a high-responsibility role that blends advanced modeling, analytical investigations and project ownership. You’ll take the lead on projects that improve the core discovery experience—such as personalisation, relevance ranking, and multi-modal retrieval—as well as conduct deep investigations into user behaviour and feature performance.

You’ll be working across the full research and experimentation lifecycle: from framing the problem and exploring the data, to prototyping models, running offline evaluations, and validating ideas through AB testing. You'll also contribute to literature reviews and research spikes—for example, investigating how to apply new developments in vector search, embeddings, or LLMs to our discovery stack.

This is a senior position, so you'll be expected to run projects independently: shaping roadmaps, communicating findings with clarity, mentoring junior team members, and collaborating closely with engineers, PMs and analysts to deliver measurable user and business impact.

We work primarily in Python and SQL, with tools like Scikit-learn, Tensorflow, PyTorch and Pandas. Our ML stack runs on AWS and Sagemaker. We value clean, documented, well-tested and reviewed code—and have the tooling and culture to support this.

Responsibilities

• Lead data science projects that improve product discovery features like search, recommendations and browsing
• Research and prototype new approaches using structured data, text, image and multi-modal embeddings
• Design and run offline evaluations to assess model changes before launch
• Conduct statistical investigations into customer behaviour and funnel performance (e.g. search abandonment, filter usage, session patterns)
• Run literature reviews and research spikes on emerging techniques—e.g. LLM-assisted retrieval, hybrid recommenders, contrastive learning
• Collaborate with ML engineers to move promising prototypes into production
• Design and analyse AB tests to evaluate impact on discovery metrics (e.g. conversion, engagement, retention)
• Present complex results to non-technical stakeholders with clarity and strategic insight
• Mentor junior data scientists, delegate tasks where appropriate, and help set technical direction

• 5+ years of experience in applied data science, preferably in search, recommendations or user modelling
• Strong Python and SQL skills, with deep experience in data exploration, feature engineering and model evaluation
• Proven experience applying and comparing models for structured prediction, ranking, retrieval or recommendation
• Strong understanding of offline evaluation techniques and trade-offs in information retrieval and recommender systems
• Ability to communicate clearly across disciplines and seniority levels—including product, design and engineering
• Experience planning and delivering projects end-to-end, from problem definition to experimentation and rollout
• Familiarity with AB testing design and analysis in online product settings
• Bonus: experience working with embeddings (e.g. image, text, product), vector search, LLMs or hybrid models