About Us
As a Fortune 50 company with more than 400,000 team members worldwide, Target is an iconic brand and one of America's leading retailers. Joining Target means promoting a culture of mutual care and respect and striving to make the most meaningful and positive impact. Becoming a Target team member means joining a community that values different voices and lifts each other up. Here, we believe your unique perspective is important, and you'll build relationships by being authentic and respectful.
Overview about TII
At Target, we have a timeless purpose and a proven strategy. And that hasn’t happened by accident. Some of the best minds from different backgrounds come together at Target to redefine retail in an inclusive learning environment that values people and delivers world-class outcomes. That winning formula is especially apparent in Bengaluru, where Target in India operates as a fully integrated part of Target’s global team and has more than 5000+ team members supporting the company’s global strategy and operations.
Pyramid Overview
A role with Target Data Science & Engineering means the chance to help develop and manage state of the art predictive algorithms that use data at scale to automate and optimize decisions at scale. Whether you join our Statistics, Optimization or Machine Learning teams, you’ll be challenged to harness Target’s impressive data breadth to build the algorithms that power solutions our partners in Marketing, Supply Chain Optimization, Network Security and Personalization rely on.
Team Overview
The Space/Presentations Data Science team builds data science capabilities that help Target make better Planogram decisions across stores. The team develops ML and Optimization models and decisioning systems that estimate Sales, understand space elasticity, optimize Planogram fitment, measure incrementality, and support POG execution strategies that balance sales, margin, guest value, competitive position, and business guardrails. Planogram is a critical lever for how guests interact with Target at stores ,spurs sales and makes enterprise growth, affordability, guest trust, and profitability. The team works at the intersection of machine learning, econometrics, forecasting, optimization, experimentation, retail science, and production decisioning to improve how prices are recommended, reviewed, measured, and scaled across categories. 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 strong 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 enviironments
- 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
- Strong experience in Python, SQL, and large-scale data analysis.
- Hands-on experience with machine learning, statistical modelling, and model validation.
- Experience with demand forecasting, elasticity modelling and optimization.
- Strong understanding of feature engineering, backtesting, model evaluation, and performance diagnostics.
- Experience working with large-scale structured data using Spark, PySpark, Hive, Hadoop, or similar platforms.
- Basic to intermediate experience with optimization methods, simulations, or constraint-based decisioning.
- Ability to translate business problems into analytical and modeling solutions.
- Strong documentation, storytelling, and stakeholder communication skills.
Preferred / Good-to-Have Skills
- Experience in retail, merchandising.
- Experience with scalable model pipelines, automated retraining, model monitoring, explainability, and MLOps practices.
- Experience with market testing, synthetic controls, double-delta measurement, or causal impact frameworks.
- 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.
- Experience with cloud platforms, APIs, containerization, workflow orchestration, MLflow, Airflow, Docker, Kubernetes, or similar tools.
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