Overview
Develop and improve scalable data science solutions for retail promotion optimization. Build models and decision engines that support personalized offers, incremental sales, redemption, and offer investment decisions.
What you'll do
- Design and evaluate models for segmentation, propensity, redemption prediction, incremental response, offer ranking, and personalization.
- Develop optimization solutions for allocating offers under business, budget, inventory, and operational constraints.
- Apply experimentation, causal inference, uplift modeling, and statistical measurement to estimate incremental impact.
- Build and productionize data science pipelines using Python, SQL, Spark, Hadoop/Hive, and ML frameworks.
- Deploy, monitor, troubleshoot, and improve models and decisioning systems in production.
- Develop simulation frameworks to test promotion strategies and compare business scenarios.
- Mentor junior data scientists and analysts and communicate model behavior, trade-offs, and business impact.
What you'll need
- 4+ years of relevant experience in data science, applied machine learning, operations research, optimization, AI engineering, or advanced analytics.
- Bachelor’s, Master’s, or PhD in a quantitative field such as Computer Science, Statistics, Mathematics, or Operations Research.
- Strong foundations in machine learning, probability, statistics, experimental design, model evaluation, and data analysis.
- Experience with operations research methods such as linear programming, mixed-integer programming, simulation, or constrained optimization.
- Strong Python and SQL skills with large-scale data platforms such as Spark, Hadoop, or Hive.
- Working knowledge of MLOps and production ML practices, including monitoring, testing, reproducibility, and CI/CD.
- Ability to communicate complex technical concepts and collaborate across teams and geographies.
Nice to have
- Experience in marketing science, promotion optimization, personalization, recommender systems, pricing, or retail media.
- Experience with causal inference, uplift modeling, incrementality measurement, A/B testing, bandits, or reinforcement learning.
- Experience with cloud platforms, containerization, workflow orchestration, feature stores, or real-time inference.
- Exposure to Generative AI, LLMs, prompt engineering, retrieval-augmented generation, or AI-assisted analytics.
- Experience mentoring junior team members or leading technical workstreams.
Details
- Location: Bangalore, India
Read the full description and apply on the company’s own careers page.
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