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Tier-1 brand, mid-level ML role, metro location, and common 3–6 year band increase applicant competition.
Core ML, deployment, and analytics skills transfer across industries, though e-commerce domain experience is beneficial.
Explicit 3–6 years plus mandatory SQL, Python, ML deployment and analytics skills make filters strict.
Lead design, build, and scaling of internal AI analytics platform including NL-to-SQL pipelines, domain knowledge bases, and dashboards.
Develop and deploy predictive models and ML features to improve product discovery, targeting, and measurement in e-commerce.
Collaborate cross-functionally to drive data-driven decision-making and deliver actionable business insights to senior leadership.
3–6 years of relevant professional experience in data science or related field.
Strong proficiency in SQL (SparkSQL / Hive), Python, and working knowledge of JavaScript for dashboard development.
BTech/MTech/MBA/MSc in Statistics, Applied Econometrics, Mathematics, Physics, Computer Science, or related field; advanced degree preferred.
Proven experience building and deploying machine learning models from prototype to production; strong quantitative and statistical skills.
Experienced in applying AI/ML techniques specifically in e-commerce product discovery or personalization.
Familiar with modern AI/ML tools including LLMs (Claude, GPT), prompt engineering, and AI deployment pipelines.
Capable of managing complex projects involving data engineering, AI integration, and stakeholder communication in fast-paced environments.