Machine Learning Data Scientist in London

In a nutshell, we’re all about making the world of wine a better place. We fund and source directly from independent winemakers to bring customers better quality wine for a better price. We’re one of the UK’s favourite wine clubs, shipping over 1 million cases a year to curious wine lovers. Our global team is entrepreneurial by nature, obsessive about customer experience and driven by performance.

We’re looking for a Data Scientist to support and develop our demand forecasting capability across our product portfolio. You’ll work alongside experienced colleagues to build, maintain, and improve forecasting models that inform planning, commercial decisions, and S&OP processes within a D2C ecommerce environment. This role is suited to someone with strong statistical foundations who is looking to deepen their experience in demand forecasting and applied machine learning.

Location & Flexible Working - London Office/Hybrid.

• Build, maintain, and improve demand forecasting models across product segments using Python and SQL, with support from senior team members.
• Apply statistical forecasting techniques including time-series models, regression methods, and introductory machine learning approaches.
• Support scenario modelling to assess the impact of promotions, pricing changes, seasonality, and uncertainty.
• Analyse demand drivers such as customer behaviour, seasonality, pricing, and commercial activity to improve forecast accuracy and robustness.
• Validate and monitor model performance to ensure outputs are accurate, reliable, and appropriate for use.
• Track and report on forecasting KPIs including accuracy, bias, and demand variability, supporting root-cause analysis where forecasts differ from actuals.
• Contribute to forecasting for new product and wine launches, using historical analogues and early performance indicators.
• Prepare and communicate forecast outputs, risks, and opportunities for S&OP discussions, ensuring insights are clear, evidence-based, and actionable.
• Partner with Platform Engineering and Data Engineering to support production data pipelines and model deployment.
• Take part in the wider Analytics & Data community, sharing learnings and contributing to improvements in team practices.

A Bachelor’s degree in Mathematics, Statistics, or a related field with 2–3 years’ experience in a D2C ecommerce environment, or a Master’s degree in Data Science with 1–2 years’ relevant industry experience.

Experience building or supporting demand forecasting models, with a solid understanding of:

• Seasonality, trend analysis, and decomposition.
• Familiarity with forecasting and machine learning approaches such as Prophet, ARIMA, and Gradient Boosting.
• Strong Python and SQL skills, including libraries such as Pandas, NumPy, Scikit-learn, Statsmodels, and Matplotlib.
• Experience using data visualisation tools.
• Comfortable working with ambiguous or imperfect data and making informed, pragmatic decisions.
• Experience collaborating with multiple technical teams to support end-to-end data and model pipelines.