Data Scientist

. Own a hard, well-defined merchandising data science problem end-to-end, from business question through production system .

Posted 9/25/2026full-timeBangalore • IndiaLeadWebsite

Key responsibilities & impact

Own a hard, well-defined merchandising data science problem end-to-end, from business question through production system
Define decision variables, recommendation logic, objectives, constraints, fallback behavior, and success criteria
Determine whether to build an automated decision engine or decision-support system
Select and implement optimization, heuristics, simulation, forecasting, causal/statistical inference, or hybrid approaches
Build production systems integrated with merchandising, planning, and platform systems
Establish methods, patterns, and quality standards for other data scientists and engineers
Define metrics and run experiments to validate improvements in accuracy, forecast error, plan executability, margin, sell-through, or recommendation adoption
Create documentation, reusable components, and decision frameworks
Identify the next important iteration of the problem
Use AI-accelerated development tools while maintaining correctness and maintainability
Communicate problems, tradeoffs, recommendations, and implications to engineering leads, merchants, and business stakeholders

What you’ll need

Bachelor's degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, or related field and 4 years' experience in an analytics-related field; OR
Master's degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, or related field and 2 years' experience in an analytics-related field; OR
6 years' experience in an analytics or related field
Track record of taking a hard, specific business question and shipping a measurable production system
Depth in optimization/decision methods, applied ML/forecasting/causal inference where relevant
Experience with demand or elasticity estimation, long-horizon or seasonal forecasting, constrained planning/execution, assortment or allocation optimization, or competitive/price response modeling
Experience in merchandising, retail, supply chain, or a comparable setting
Ability to build automated decision systems and human decision-support systems
Ability to improve others' work through review, mentorship, or reusable standards
Sound judgment regarding optimality versus robust heuristics and automation versus human decision support
Individual-contributor leadership through influence, not authority

Comp & perks