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Tier-1 brand, mid-level ML role, metro location, and broad skill requirements increase candidate competition.
Requires ML/OR and retail promotion domain expertise, so cross-industry transferability is moderate (medium).
Explicit 4+ years plus mandatory ML, OR, and MLOps production experience makes shortlisting highly strict.
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Own end-to-end design, development, deployment, and improvement of scalable data science solutions for retail promotion optimization affecting offer targeting and investment decisions.
Develop machine learning and optimization models to improve incremental sales, offer redemption, personalization, and business constraint adherence across the promotion ecosystem.
Lead productionalization, monitoring, experimentation, and performance measurement of models while mentoring junior staff and shaping reusable frameworks for model operations and decision intelligence.
Bachelor’s, Master’s, or PhD in Computer Science, Statistics, Mathematics, Operations Research, Industrial Engineering, Economics, Physics, Applied Sciences, or related quantitative field.
4+ years of relevant experience in data science, applied ML, operations research, optimization, AI engineering, or advanced analytics.
Proficiency in Python and SQL; experience with large-scale data platforms like Spark, Hadoop, or Hive and ML frameworks such as scikit-learn, XGBoost, TensorFlow, PyTorch, or optimization tools like Gurobi, CPLEX.
Experience building and deploying production ML models or algorithmic systems that deliver measurable business outcomes, including knowledge of MLOps practices and CI/CD.
Experienced in integrating machine learning, operations research, and experimentation to solve complex promotion personalization and optimization problems in a retail or e-commerce environment.
Comfortable balancing scientific rigor with business pragmatism, capable of translating ambiguous marketing challenges into algorithmic solutions with clear business impact.
Familiar with production-scale ML workflows, model monitoring, and deployment in a collaborative, cross-functional, and geographically distributed team environment.