Senior Data Scientist – Hybrid, London

Who We Are

Datassential is a leading global intelligence platform for the food and beverage industry. Leveraging billions of data points and cutting‑edge AI, we provide a suite of innovative solutions that empower more than 90% of the world's largest food and beverage brands to develop, market, and sell their products more effectively.

Job Location

This role is hybrid and based in London, with regular on‑site presence required 2‑3 days per week. Candidates must be located within commuting distance of our London office.

Why You Should Join Us

Our data science team works with one of the most distinctive datasets in the food and beverage industry: deep, longitudinal operator data spanning years of menu, concept, and market behaviour across global markets. The EMEA region is a growth priority for Datassential, and this role sits at the centre of how we build and deliver intelligence for that market. You will have the rare combination of genuine domain depth to draw on, a wealth of historical data to work with, and a direct line to how your outputs reach customers through our sales intelligence platform. We are passionate about data, committed to doing the work well, and have a "we can do anything" attitude. We value work‑life balance, and you will be joining a team and company that wants you to grow.

Responsibilities

• Design, develop, and validate statistical models and machine learning solutions that generate derivative data elements and insights for the EMEA sales intelligence platform.
• Work across diverse data sources—including operator, menu, concept, and market data—to identify signals, build features, and produce modelled outputs that enrich our core datasets.
• Own the full modelling lifecycle: problem framing, data exploration, feature engineering, model selection, validation, iteration, and operationalisation.
• Collaborate with the data pipeline team to integrate model outputs into production Alteryx workflows, ensuring reliable, repeatable delivery of scored and derived data.
• Apply current ML and AI techniques appropriately—including where LLM‑based approaches can augment traditional modelling—while maintaining rigorous statistical standards.
• Leverage Datassential's broad‑based and longitudinal foodservice dataset to build models that benefit from historical depth and evolve as new data is acquired.
• Translate complex modelling outputs into clear, actionable insights for internal stakeholders and, where appropriate, for customer‑facing intelligence products.
• Contribute to the development of reusable modelling frameworks and standards that can be applied consistently across EMEA data products.
• Provide technical guidance to other data scientists on the team, supporting their growth in modelling rigour and best practice.
• Collaborate with Sales, Product, and Client teams to understand the intelligence needs of EMEA customers and translate them into modelling priorities.
• Stay current with relevant advances in ML, AI, and statistical methodology—and make grounded judgements about when new approaches are worth adopting.
• Document model design decisions, assumptions, validation results, and known limitations in a way that supports maintainability and auditability.
• Work with global data science colleagues to ensure EMEA modelling approaches are consistent with and complementary to the broader platform.

Qualifications

• Demonstrated experience in data science, quantitative modelling, or a closely related role, with a track record of taking models from development to production.
• Strong proficiency in Python for data science work: data manipulation, feature engineering, model development, and validation (pandas, scikit‑learn, and equivalent libraries).
• Strong SQL skills across one or more environments.
• Demonstrated command of statistical fundamentals: regression, classification, clustering, time‑series analysis, hypothesis testing, and model evaluation.
• Hands‑on experience with modern ML techniques and the judgement to select the right approach for a given problem.
• Experience operationalising models—not just building them—including handoff into production data pipelines.
• Familiarity with LLM‑based approaches and where they complement or augment traditional modelling.
• Ability to communicate model logic, assumptions, and outputs clearly to both technical and non‑technical audiences.
• Experience working with longitudinal or time‑series datasets where historical depth is a modelling asset.
• Minimum of BA/BS in a quantitative field (Statistics, Mathematics, Computer Science, Economics, or equivalent); advanced degree preferred.
• Ability to work effectively across time zones in a remote‑first, globally distributed team.

Nice to Have

Domain experience in foodservice.