Sr Data Scientist-Pricing

About Us

As a Fortune 50 company with more than 400000 team members worldwide Target is an iconic brand and one of Americas 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 youll build relationships by being authentic and respectful.

Overview about TII

At Target we have a timeless purpose and a proven strategy. And that hasnt 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 Targets global team and has more than 4000 team members supporting the companys 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 youll be challenged to harness Targets 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 Pricing Data Science team builds data science capabilities that help Target make better pricing decisions across stores and digital channels. The team develops models and decisioning systems that estimate demand understand price elasticity optimize recommendations measure incrementality and support pricing strategies that balance sales margin guest value competitive position and business guardrails.
Pricing is a critical lever for 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 Pricing you will help build and improve data science models that power Targets 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

• Bachelors Masters 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.

Know More About Us here:

• Life at Target- Experience:

  Senior IC