Data Scientist – Category Management (with ML/AI Focus) About the Role: Quest Search & Selection is working with a leading tech-driven eCommerce platform specializing in consumer goods and rapid delivery. Offering a wide product assortment; including groceries, household essentials, over-the-counter medicine, office supplies, and fresh food. The business leverages data and innovation at scale to drive customer satisfaction and operational excellence. We are seeking a Data Scientist – Category Management with a strong foundation in machine learning and applied AI to join the Analytics & Insights team. In this role, you'll lead data science initiatives that optimize product availability, buying strategies, and category performance using advanced statistical modeling and machine learning techniques. You will work closely with Category Managers, In-Stock Merchandising, and a dedicated Data Analyst to deliver intelligent automation and data-driven decision-making at scale. Key Responsibilities: Develop and deploy machine learning models to forecast product demand, manage availability, and predict expiration or wastage risks. Build and iterate on AI-powered decision support tools to optimize inventory management and buying policies. Design and execute experiments (A/B tests) to validate model performance and drive improvements in stock ordering strategies. Collaborate with Category and In-Stock teams to evolve the Stock Ordering Tool using intelligent algorithms and real-time data. Analyze the impact of availability levels on revenue and order volumes using statistical and predictive techniques. Create automated pipelines for monitoring performance of buying policies and flagging anomalies or risks in product lifecycle. Lead post-implementation reviews with insights into the performance of AI/ML systems and identify opportunities for further optimization. Set and tune dynamic targets for availability and expiry rates using continuous learning models. Guide and mentor a Data Analyst to support ongoing analysis, data pipeline development, and dashboarding. Key Requirements: 4–6 years of experience in data science, analytics, or ML engineering, ideally in consumer products, retail tech, or supply chain. Solid understanding of supervised learning, time-series forecasting, clustering, and other core ML techniques. Strong experience with SQL and querying large, complex datasets. Proficiency in Python (Pandas, scikit-learn, XGBoost, etc.); familiarity with R or dbt is a plus. Experience working with BI tools such as Looker, Tableau, or Power BI. Comfort working in high-growth environments (start-ups or scale-ups preferred). Experience designing and interpreting experiments (e.g., A/B testing) and causal inference analysis. Degree in a quantitative field such as Mathematics, Statistics, Computer Science, Data Science, or Engineering. Benefits: Comprehensive medical and dental insurance. Equity participation through company shares/RSUs. Annual bonus tied to performance. Hybrid work model (3 days in office, flexible remote days). Exclusive employee discounts. Clear path for career progression in a high-impact, data-first team. Annual performance reviews and merit-based bonuses.