Role Overview As a Senior Data Scientist in Pricing, you will help build and improve data science models that power Target's Price Optimization capabilities. The primary focus of this role will be price elasticity modeling,demand forecasting, and optimization-based pricing recommendations. You will partner with Data Scientists, Product Managers, Engineers, Analysts, Merchandising partners, and business stakeholders to translate complex pricing problems into scalable modeling solutions. You will develop models that estimate how guests respond to price changes, forecast demand under different pricing scenarios, support recommendation generation, and measure the incremental impact of pricing decisions. 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 pricing 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 price elasticity models that estimate guest response to price changes across items, markets, channels, and categories. Build demand forecasting models that account for price, seasonality, promotions, competitive signals, item hierarchy, inventory, store or market behavior, and historical sales patterns. Design pricing recommendation models that balance revenue, margin, guest affordability, competitive positioning, guardrails, and business constraints. Apply machine learning, statistical modeling, econometrics, time series forecasting, causal inference, and optimization techniques to solve pricing problems at scale. Explore approaches such as log-log elasticity models, panel models, hierarchical models, generalized additive models, gradient boosting, Bayesian methods, and other relevant ML techniques. Partner with business and product teams to understand pricing strategy, define success metrics, and translate requirements into model design. Work with large-scale retail data including sales, price history, promotions, item attributes, competitor pricing, 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. Build simulation and scenario-testing frameworks to evaluate pricing strategies before recommendations are deployed. 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, econometrics, forecasting, optimization, pricing science, or decision science. Strong hands-on experience building and validating machine learning or statistical models in a business setting. Experience with price elasticity modeling, demand modeling and forecasting. Strong understanding of statistical concepts,model evaluation,feature engineering, regularization, cross-validation, uncertainty, and model interpretability. Experience with time series forecasting, causal inference, experimentation and measurement. 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. Practical understanding of optimization concepts such as constrained optimization, linear programming, mixed-integer programming, heuristics, simulation, or scenario planning. 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 modeling, and model validation. Experience with price elasticity modeling, demand forecasting, pricing analytics 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. Good understanding of experimentation, A/B testing, causal measurement, or incrementality analysis. 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 pricing,merchandising, revenue management, promotions, competitive intelligence,assortment,inventory, or e-commerce. Experience with advanced elasticity techniques such as hierarchical models, Bayesian models, GAMs, panel regression, causal models, or mixed-effects models. Experience with forecasting frameworks, time series models, demand sensing, or price-demand response modeling. Experience with optimization techniques such as linear programming, mixed-integer programming, nonlinear optimization, simulation-based optimization, or heuristics. Experience with scalable model pipelines, automated retraining, model monitoring, explainability, and MLOps practices. Experience with market testing, synthetic controls, CUPED, 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.

About Target

Target is one of the world’s most recognized brands and one of America’s leading retailers. We make Target our guests’ preferred shopping destination by offering outstanding value, inspiration, innovation and an exceptional guest experience that no other retailer can deliver. Target is committed to responsible corporate citizenship, ethical business practices, environmental stewardship and generous community support. Since 1946, we have given 5 percent of our profits back to our communities. Our goal is to work as one team to fulfill our unique brand promise to our guests, wherever and whenever they choose to shop.