Sr Data Scientist- Promo Optimisation

About Target
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 while striving to make a meaningful and positive impact for our guests team members and communities. Becoming a Target team member means joining a community that values diverse voices encourages authenticity and lifts each other up.

About Target in India
At Target we have a timeless purpose and a proven strategy. Some of the best minds from different backgrounds come together to redefine retail in an inclusive learning environment that values people and delivers world-class outcomes. This winning formula is especially apparent in Bengaluru where Target in India operates as a fully integrated part of Targets global team with more than 5000 team members supporting Targets global strategy and operations.

Pyramid Overview
A role with Target Data Science & Engineering offers the opportunity to develop deploy and operate state-of-the-art algorithmic systems that use data at scale to automate personalize and optimize business decisions. Our teams apply machine learning statistics operations research simulation optimization and AI engineering to solve complex retail problems across Marketing Personalization Supply Chain Merchandising Finance Network Security and Guest will work with Targets rich data ecosystem and partner with product engineering analytics and business teams to create scalable decisioning systems that deliver measurable business and guest impact.

Team Overview
The Promo Optimization team Calibrate owns and evolves Targets promotion segmentation and offer optimization engine. The team powers personalized offers and offer depths for Target Circle guests across the retail assortment. We build machine learning causal inference optimization simulation and Generative AI capabilities to decide which guests should receive which offers at what value through which channels and under what business constraints.
The team is responsible not only for model development but also for production deployment monitoring experimentation performance measurement and continuous improvement. Our work sits at the intersection of Marketing Science personalization decision optimization and enterprise-scale AI platforms.

Position Overview
As a Senior Data Scientist you will be a hands-on technical contributor and problem solver responsible for designing developing deploying and improving scalable data science solutions for retail promotion optimization. You will work across machine learning operations research experimentation and AI engineering / MLOps to build models and decision engines that drive incremental sales improve redemption optimize offer investment and enhance guest relevance.
You will collaborate closely with product managers engineers data scientists analysts and Marketing business partners to translate ambiguous business problems into well-defined analytical algorithmic and engineering solutions. You will contribute to the full lifecycle of data science products: opportunity sizing data exploration model development optimization formulation offline evaluation production deployment monitoring and iterative improvement.
You will also mentor junior team members uphold high standards for scientific rigor and engineering quality and help shape reusable frameworks for experimentation simulation model operations and decision intelligence.

Role Overview

• Develop a deep understanding of Targets promotion ecosystem guest behavior Marketing objectives offer funding incremental sales redemption and business constraints.

• Translate business problems in promotion personalization and offer optimization into data science machine learning causal inference simulation and mathematical optimization problems.

• Analyze large-scale structured and unstructured datasets to identify guest item offer channel and campaign patterns that can improve model performance and business outcomes.

• Design build and evaluate machine learning models for segmentation propensity redemption prediction incremental response offer ranking personalization and guest-level decisioning.

• Develop optimization-based solutions using linear programming mixed-integer programming constrained optimization stochastic optimization simulation and heuristic approaches to allocate offers under business budget inventory guest experience and operational constraints.

• Apply experimentation causal inference uplift modeling and statistical measurement techniques to estimate incremental impact validate model decisions and guide business trade-offs.

• Build and productionalize scalable data science pipelines using Python SQL Spark Hadoop/Hive and modern ML frameworks.

• Partner with engineering teams to deploy models and decisioning modules into production systems with strong attention to reliability scalability latency observability and maintainability.

• Contribute to AI engineering and MLOps capabilities including feature pipelines model versioning automated training batch and real-time scoring model monitoring drift detection alerting retraining workflows and performance dashboards.

• Use Generative AI and advanced AI techniques where appropriate to accelerate experimentation improve decision support enhance model development workflows or create scalable business-facing tools.

• Develop and maintain simulation frameworks to test promotion strategies compare scenarios evaluate business constraints and understand downstream impact before production rollout.

• Troubleshoot issues in data pipelines model outputs optimization results and production systems; identify root causes and implement durable fixes.

• Create clear documentation model evaluation summaries experiment readouts and technical narratives that help stakeholders understand model behavior trade-offs risks and business impact.

• Collaborate across geographies and time zones with data scientists engineers product managers analysts and business partners.

• Mentor junior data scientists and analysts by reviewing approaches improving code quality strengthening analytical rigor and helping them connect data science methods to business outcomes.

• Stay current with developments in machine learning operations research Generative AI experimentation and MLOps and apply relevant techniques to Targets retail challenges.

About You

• Bachelors Masters or PhD in Computer Science Statistics Mathematics Operations Research Industrial Engineering Economics Physics Applied Sciences or a related quantitative field.

• 4 years of relevant experience in data science applied machine learning operations research optimization AI engineering or advanced analytics.

• Hands-on experience applying machine learning techniques to solve business problems preferably in retail e-commerce Marketing personalization promotions pricing supply chain advertising or customer decisioning.

• Strong foundation in supervised and unsupervised learning probability statistics experimental design model evaluation and data analysis.

• Experience with one or more areas of operations research such as linear programming mixed-integer programming constrained optimization simulation stochastic processes heuristic optimization or decision science.

• Experience building and deploying models or algorithmic systems that create measurable business impact in production environments.

• Strong programming skills in Python and SQL with experience working with large-scale data platforms such as Spark Hadoop Hive or cloud-based distributed data environments.

• Experience with ML frameworks and libraries such as scikit-learn XGBoost TensorFlow PyTorch OR-Tools Pyomo Gurobi CPLEX or similar tools.

• Working knowledge of MLOps and production ML practices including model versioning automated pipelines testing monitoring reproducibility CI/CD observability and model governance.

• Ability to clean transform join and analyze large datasets and convert complex data into actionable insights and production-ready features.

• Strong software engineering discipline including modular code design testing code reviews documentation source control and collaborative development practices.

• Ability to balance scientific rigor with practical business delivery making thoughtful trade-offs between model complexity explainability scalability and operational feasibility.

• Strong analytical thinking structured problem-solving and data visualization skills.

• Ability to communicate complex technical concepts clearly to technical and non-technical audiences.

• Self-driven curious and results-oriented with the ability to operate in ambiguous problem spaces and deliver against tight timelines.

• Strong collaboration skills and the ability to work effectively across teams functions geographies and time zones.

Preferred Qualifications

• Experience in Marketing science promotion optimization personalization recommender systems pricing retail media customer targeting or offer decisioning.

• Experience with causal inference uplift modeling incrementality measurement A/B testing multi-armed bandits or reinforcement learning.

• Experience designing optimization solutions that account for real-world business constraints such as budget eligibility inventory guest experience campaign objectives fairness or operational capacity.

• Experience building reusable simulation experimentation or decisioning frameworks.

• Experience with cloud platforms containerization workflow orchestration feature stores model registries or real-time inference systems.

• Exposure to Generative AI LLMs prompt engineering retrieval-augmented generation agents or AI-assisted analytics / decision support.

• Experience mentoring junior team members or leading technical workstreams.

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• Life at Target- Experience:

  Senior IC