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Protocol Intelligence
Data-driven signals on your job's competitivenessTier-1 employer and Bangalore location increase applicant density, but senior specialized role limits generalist competition.
Role requires retail/merchandising and supply-chain experience plus specialized ML/optimization, so backgrounds transfer poorly across unrelated industries.
Explicit senior experience options and specialized production ML, optimization, and retail forecasting requirements create stringent filters.
Job Description
Structured overview of role & requirementsAbout This Role
Own a specific, complex merchandising data science problem end-to-end, translating business questions into production systems that improve decision-making or automate decisions.
Design, build, and maintain production-quality decision engines or decision support systems integrating with merchandising and planning platforms, ensuring measurable improvement in key metrics.
Establish standards, documentation, and reusable components to enable ongoing progress beyond individual contribution, and communicate insights and tradeoffs effectively to technical and business stakeholders.
Minimum Requirements
Bachelor's degree in Statistics, Economics, Analytics, Mathematics, Computer Science, IT, or related field plus 5 years' experience in analytics; OR Master's degree in the above fields plus 3 years' experience; OR 7 years' experience in analytics or related field.
Strong expertise in optimization/decision methods (mathematical programming, heuristics, simulation) and applied machine learning, forecasting, or causal inference.
Experience with demand/elasticity estimation, seasonal or long-term forecasting, constrained planning/execution, assortment/allocation optimization or competitive pricing modeling in merchandising, retail, or supply chain contexts.
Work Experience Required: Specified as above; notice period not explicitly mentioned.
Ideal Candidate Profile
Individual contributor capable of leading on hard, well-defined merchandising data science problems with measurable impact, without managing teams.
Expertise in both automated decision-making systems and decision support tools for human users such as merchants or planners.
Skilled at influencing and raising standards of peers through mentorship, reviews, and establishing quality patterns, with a focus on accountability for adoption and ongoing iteration.