Senior Data Scientist

Responsibilities:

• Build and maintain demand and sales forecasting models across products, stores and channels to support planning, replenishment and commercial decisions.
• Develop pricing models, including price elasticity, price optimisation, and competitive price analysis, to improve margin and sales.
• Measure promotion effectiveness (lift, cannibalisation, ROI) and build tools that help teams plan better promotions.
• Design experiments and use causal inference methods to measure the real impact of pricing and promotional actions.
• Explore and apply modern AI (LLMs, RAG, embeddings, GenAI tools) to improve models, automate insights and support business users.
• Take models from prototype to production with Data Engineering and ML Engineering, including monitoring and retraining.
• Contribute to team best practices, code reviews and knowledge sharing.

Requirements:

• 3-6 years of experience in data science or applied machine learning, ideally in retail, e-commerce or CPG.
• Bachelor's or Master's degree in Statistics, Mathematics, Economics, Computer Science, Engineering or a related field.
• Strong Python skills and experience with common data science libraries (e. g., pandas, polars, numpy, scikit-learn).
• Experience with the full ML lifecycle: data preparation, training, evaluation, deployment and monitoring.
• Hands-on experience with demand or sales forecasting using statistical and/or machine learning methods.
• Experience in pricing analytics, such as price elasticity, price optimisation, or competitive pricing.
• Experience analysing or optimising promotions.
• Solid understanding of experimentation and causal inference (A/B testing, quasi-experimental methods).
• Hands-on experience with a cloud platform for ML and data science (GCP preferred).
• Practical experience with modern AI, e. g., LLMs, RAG, embeddings or GenAI-based tools.

Should have:

• Proficiency in Python/SQL and comfort working with large datasets.
• Experience deploying models into production environments.
• Understanding of optimisation techniques (e. g., linear programming, constrained optimisation).
• Exposure to CI/CD and model versioning practices.
• Strong communication skills and the ability to explain technical results to non-technical audiences.

Good to have:

• Familiarity with time-series foundation models or deep learning for forecasting.
• Experience building AI agents or LLM-powered tools for business users.
• Experience with dynamic pricing, markdown optimisation.

Tools and Technologies (indicative):

• ML and AI: Vertex AI or equivalent ML platforms, Hugging Face.
• Data: BigQuery or similar data warehouses, Cloud Storage.
• Orchestration and CI/CD: Airflow, GitHub/GitLab Actions.