Senior Data Scientist

Responsibilities:

• Frame the decision. Turn ambiguous questions into clear decision problems, objectives, constraints, hypotheses, success measures, and analytical plans tied to business outcomes.
• Build robust forecasts. Develop time-series, machine-learning, hierarchical, probabilistic, and cold-start approaches; quantify uncertainty across relevant levels and horizons, and backtest performance.
• Measure marketing impact. Design experiments and apply causal-inference and quasi-experimental methods to estimate incrementality, causal impact, and return from marketing, publicity, promotions, and other interventions.
• Optimise pricing and investment. Use elasticity models, simulation, mathematical optimisation, and scenario analysis to improve pricing, promotion, and resource-allocation decisions within business constraints.
• Optimize supply-chain decisions. Translate demand distributions into practical inventory, production, reprint, allocation, buffer-stock, and warehouse recommendations that balance service, cost, and risk.
• Use AI to accelerate delivery. Apply approved generative AI and AI-assisted tools to research, code, develop features, test, document, and prototype faster; establish evaluation and human-review controls.
• Own the solution lifecycle. Build, package, deploy, monitor, troubleshoot, and maintain models, decision services, and automated workflows, including testing, versioning, drift monitoring, and refresh processes.
• Drive adoption and value. Communicate recommendations, uncertainty, and trade-offs clearly; embed solutions into workflows, measure realised outcomes, and mentor colleagues.

Requirements:

• Relevant experience. 6+ years in decision science, data science, applied economics, operations research, forecasting, quantitative analytics, or a related field.
• Forecasting depth. Advanced Python and SQL with strong experience in time-series modelling, probabilistic forecasting, hierarchical forecasting, temporal validation, and model evaluation.
• Causal measurement. Experience estimating marketing incrementality and causal impact through experimentation, quasi-experimental methods, or causal inference.
• Optimisation depth. Experience with pricing, elasticity, mathematical optimisation, simulation, inventory, supply chain, resource allocation, or related constrained decisions.
• End-to-end production capability. Experience moving models from notebook to production using modular code, APIs or batch workflows, packaging or containers, automated tests, Git, CI/CD, model tracking, monitoring, and cloud deployment.
• Practical AI experience. Demonstrated use of generative AI or AI-assisted development to accelerate analytical work or product delivery, with systematic evaluation and responsible-use controls.
• Business leadership. Ability to connect model outputs to decisions, balance rigour with speed, communicate uncertainty and trade-offs, and lead across technical and business teams.

Preferred:

• Modern solution patterns. Experience with orchestration, feature management, real-time services, automated retraining, model registries, or decision-support applications.
• Industry or academic background. Publishing, media, retail, or consumer-products experience; advanced quantitative degree or equivalent experience.

Skills

Causal Inference, Forecasting, Python, SQL, Time-Series