Sr Data Scientist- Space Presentation

Overview

Senior Data Scientist (Space/Presentations) focused on building and improving ML and Optimization models for Target’s Planogram capabilities in Merchandising.

What you'll do

• Develop, validate, and improve forecasting and elasticity models to estimate sales for Planogram facings recommendations.
• Use optimization to recommend optimal item placements on POG using constrained linear programming with fuzzy logic constraints.
• Create item groups/segments to measure POG performance and recommend changes using segmentation and similarity measures.
• Scale and deploy solutions to production environments.
• Build measurement frameworks to evaluate model performance and support experimentation.
• Monitor model performance over time, identify drift/degradation, and recommend improvements.
• Communicate model logic, assumptions, trade-offs, risks, and recommendations to technical and non-technical stakeholders.

What you'll need

• 4+ years of relevant experience in data science, applied machine learning, forecasting, optimization, or retail domain knowledge.
• Strong hands-on experience building and validating ML or statistical models in a business setting.
• Experience with demand forecasting, elasticity modeling, and optimization.
• Strong understanding of statistical concepts, model evaluation, feature engineering, regularization, cross-validation, uncertainty, and interpretability.
• Optimization experience including constrained optimization, linear programming, and mixed-integer programming.
• Strong programming skills in Python and SQL, and experience working with large datasets using Spark-like tooling.
• Ability to work with large-scale structured retail data and diagnose model issues.

Nice to have

• Experience with scalable model pipelines, automated retraining, model monitoring, explainability, and MLOps practices.
• Exposure to Generative AI and LLM applications including prompt engineering, RAG, embeddings, vector databases, evaluation, and workflow automation.
• Exposure to agentic AI systems including AI agents, tool use, LangGraph, LangChain, LlamaIndex, and human-in-the-loop workflows.
• Experience building explainability, monitoring, or decision-support tools for business users.

Details

• Location: Bangalore, India.