Data Scientist - Gen AI

Own the scientific and delivery backbone of data science engagements: problem structuring, analytical design, modelling strategy, validation framework, and translation of insights into business decisions. You will define what “good” means analytically, ensure methodological rigor from exploration through deployment, and deliver solutions that are robust, interpretable, and commercially impactful. You will build production-intent analytical solutions and partner with Engineering to operationalise and scale them into enterprise-grade assets. 

Client Context 

This role will lead the analytical delivery of a sales decomposition engagement, focused on attributing revenue drivers and informing commercial decision-making. 

Key Responsibilities 

• Translate business challenges into structured analytical problems, testable hypotheses, measurable objectives, and clear scope boundaries (including risks and assumptions). 

• Ensure high-quality outputs, adherence to timelines, and proactive management of client expectations. 

• Design and execute end-to-end analytical workflows: data sourcing, cleaning, quality control, exploratory analysis, feature engineering, modelling, validation, and insight generation. 

• Own the development of retail sales decomposition solutions, selecting appropriate approaches (price and promo elasticity, marketing mix, hierarchical/panel regression, time-series models) to attribute revenue drivers and quantify incremental impact. 

• Define evaluation strategies aligned to commercial impact; establish metrics, validation methodology, acceptance thresholds, and robustness standards (cross-validation, sensitivity analysis, bias checks, interpretability) to ensure production-ready deployment. 

• Own experimentation and model improvement cycles: structured testing, benchmarking, feature iteration, and performance tracking. 

• Deliver production-ready solutions: reproducible code, version-controlled workflows, documentation, monitoring plans, and engineering-ready handoff. 

• Clearly articulate modelling assumptions, limitations, and risk considerations to stakeholders. 

• Present analytical findings to clients and senior stakeholders, breaking down complex technical concepts into clear, non-technical insights and actionable recommendations.