Sr Data Scientist- Space Presentation

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 5000 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 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 Targets 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 POGs 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

• 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 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.

Know More About Us here:

• Life at Target- Experience:

  Senior IC