Data Scientist, Product to Market (P2M) Optimization

About the Role

This is a hands-on role for someone early in their career who has strong quantitative and programming fundamentals and wants to solve real-world retail problems at scale. The team operates in a fast-paced environment where priorities evolve and problems are not always fully defined. Success requires curiosity, initiative, and self-motivation, with a willingness to take ownership, learn quickly, and proactively move work forward. The candidate will work with experienced data scientists and cross-functional partners while building deeper expertise in applied data science and optimization.

What You'll Do

• Develop and test machine learning, optimization, and statistical models for retail P2M problems
• Analyze large datasets and develop analytical solutions using Python, SQL, and related technologies
• Write reliable, maintainable code and contribute to testing, documentation, and code management
• Evaluate model performance and support measurement of business impact using appropriate statistical methods
• Work with senior data scientists to troubleshoot, improve, and operationalize analytical solutions
• Collaborate with business, product, engineering, and data science partners to understand requirements and communicate analytical findings

Who You Are

• Master’s degree in Operations Research, Operations Management, Industrial Engineering, Applied Mathematics/Statistics, Data Science, or a related quantitative field
• 2 to 3 years of experience in data science, machine learning, operations research, or applied analytics
• Experience working on retail, supply chain, pricing, inventory, merchandising, or related business problems
• Strong foundation in machine learning, statistics, and quantitative problem solving
• Hands-on proficiency in Python and SQL
• Self-motivated with demonstrated initiative and the ability to operate effectively in a fast-paced, evolving environment
• Strong analytical, communication, and collaboration skills

Preferred Qualifications

• Experience with mathematical optimization, including linear or mixed-integer programming, and tools such as Gurobi, Pyomo, OR-Tools, or equivalent
• Exposure to PySpark, Databricks, GCP, Git, or production data science environments
• Familiarity with experimentation or causal methods for measuring business impact
• Exposure to building AI agents or agentic workflows using large language models