Description
As a Senior Data Scientist in Merchandising at Target, you will help shape how the company makes pricing and clearance decisions by turning complex retail challenges into measurable business impact.
You will build scalable AI/ML solutions that improve recommendation quality, decision efficiency, and financial outcomes. Collaborating with Data Scientists, Product Managers, Engineers, Analysts, Merchandising partners, and business stakeholders, you will translate complex retail challenges into scalable, production-ready AI/ML solutions.
This role is ideal for someone with strong foundations in Machine Learning, forecasting, statistical modeling, and applied optimization, along with hands-on experience in Generative AI, LLM applications, or Agentic AI.
Key Responsibilities:
- Design and develop advanced AI/ML solutions for retail demand forecasting, price elasticity modeling, markdown optimization, and intelligent pricing decisions.
- Leverage advanced Machine Learning techniques, including ensemble learning, gradient boosting, deep learning, and time-series forecasting, to capture complex demand patterns and pricing sensitivities.
- Develop Generative AI and Agentic AI solutions leveraging LLMs, RAG, tool calling, and multi-agent architectures for automated model diagnostics, explainability, and intelligent decision-making.
- Build simulation, scenario planning, and what-if analysis frameworks to evaluate alternative pricing strategies, forecast outcomes, business trade-offs, and potential financial impact.
- Develop intelligent decision optimization frameworks combining predictive modeling, mathematical optimization, and business constraints to recommend optimal pricing and markdown strategies at scale.
- Design robust model evaluation and experimentation frameworks using forecast accuracy, bias analysis, A/B testing, causal inference, and counterfactual measurement to quantify business impact.
- Work with large-scale retail data to identify demand patterns, diagnose model performance, and continuously improve recommendation quality.
- Build automated model monitoring and evaluation capabilities to identify performance degradation, data quality issues, and emerging business trends.
- Partner with business, product, and engineering teams to translate complex retail challenges into scalable AI-powered solutions.
- Collaborate with engineering teams to productionize models, automate pipelines, and integrate AI/ML solutions into enterprise decision-making workflows.
- Drive innovation by exploring emerging AI technologies, modern forecasting architectures, and autonomous decision intelligence capabilities.
- Develop, deploy, and operationalize Machine Learning solutions on cloud platforms such as Google Cloud Vertex AI (preferred), AWS, or Microsoft Azure.
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, or AI-driven solutions.
- Strong foundation in Machine Learning, statistical analysis, predictive modeling, model validation, and mathematical optimization.
- Hands-on experience with Generative AI, LLM applications, or Agentic AI frameworks.
- Experience with simulation techniques, scenario planning, and data-driven decision-support systems.
- Strong programming expertise in Python and SQL, with experience processing large-scale datasets using distributed computing frameworks.
- Experience developing scalable ML solutions and familiarity with cloud platforms, preferably GCP and Vertex AI.
- Strong analytical thinking, problem-solving abilities, and the ability to navigate ambiguous business challenges.
- Excellent communication and collaboration skills, with the ability to partner across technical and business teams.