Role Overview

As a Senior Data Scientist in Merchandising, you will help build and improve data science ML and Optimization models that power Target's Planogram capabilities. The primary focus of this role will be Sales Forecasting and elasticity models with optimization-based presentation recommendations. You will partner with Data Scientists, Product Managers, Engineers, Analysts, Merchandising partners, and business stakeholders to translate complex problems into scalable modelling solutions. This role is ideal for someone with robust foundations in machine learning, statistical modeling, forecasting, and applied optimization, with interest in solving high-impact retail problems at scale. Experience with Generative AI, LLMs, RAG, or AI agents is a plus as the team explores AI-enabled measurement, explainability, monitoring, and decision-support workflows.

Key Responsibilities

- Develop, validate, and improve forecasting and elasticity models (using Regressions) that estimate Sales which is used as input for facings recommendations on Planogram.
- Account for multiple variables present in forecasting and separate impact of target variable on Sales (VIF, multicollinearity).
- Use optimization to recommend optimal item placements on POG such that expense to service POG's is lower and all item facings which are recommended fit on the POG (constrained Linear programming including the use of Fuzzy logic constraints).
- Create Item groups/segments to measure POG Performance and recommend changes using segmentation and similarity measures.
- Scale and deploy solution to production environments.
- Create measurement frameworks to evaluate model performance.
- Partner with business and product teams to understand strategy, define success metrics, and translate requirements into model design.




- Work with large-scale retail data including sales, presentation history, item attributes, inventory, store and market attributes, and guest demand signals.
- Conduct deep-dive analyses to diagnose model performance, elasticity behavior, underperforming recommendations, outliers, sparse data, and category-specific pricing patterns.
- Support experimentation and measurement design, including A/B tests, market tests, incrementality measurement, control/test methodology, and model impact assessment.
- Collaborate with ML Engineers and Software Engineers to productionize models, automate pipelines, improve reliability, and integrate outputs into business-facing workflows.
- Monitor model performance over time, identify drift or degradation, and recommend improvements to maintain model quality and business impact.
- Communicate model logic, assumptions, trade-offs, risks, and recommendations clearly to technical and non-technical stakeholders.
- Contribute to model explainability and adoption by helping business partners understand why recommendations are generated.
- Explore GenAI, LLMs, RAG, and agents for pricing use cases such as explainability, measurement automation, performance monitoring, and recommendation efficiency.

About You

- Bachelor's, Master's, or PhD in Data Science, Statistics, Economics, Mathematics, Operations Research, Computer Science, Engineering, or a related quantitative field.




- 4+ years of relevant experience in data science, applied machine learning, forecasting, optimization, retail domain knowledge, GCP, Big Data.
- Strong hands-on experience building and validating machine learning or statistical models in a business setting.
- Experience within Merchandising on elasticity modeling, demand modeling and forecasting.
- Strong understanding of statistical concepts, model evaluation, feature engineering, regularization, cross-validation, uncertainty, and model interpretability.
- Experience with optimization such as constrained optimization, linear programming, mixed-integer programming.
- Experience with experimentation and measurement.
- Ability to work on Big Data.
- Ability to scale solutions to production environments.
- Strong programming skills in Python and SQL, with experience working on large datasets using Spark, PySpark, Hive, Hadoop, or similar platforms.
- Ability to analyze complex data, diagnose model issues, and convert findings into actionable recommendations.
- Ability to work in ambiguous problem spaces, structure analytical approaches, and deliver high-quality outcomes against business timelines.
- Strong communication and collaboration skills, with the ability to partner across Data Science, Product, Engineering, Analytics, Merchandising, and business teams.

Must-Have Skills

- Python
- SQL
- Machine Learning
- Forecasting
- Elasticity Modelling
- Optimization
- Spark
- PySpark
- Hive

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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