Overview Principal Data Scientist, Value Chain Optimization (Ref: 191387) — This opportunity focuses on enhancing supply chain efficiencies through advanced analytics and optimization within the apparel and accessories retail sector. The role requires leveraging data science expertise to address complex problems and align with strategic business objectives. Salary and Benefits Base pay range: $230,000.00/yr - $275,000.00/yr Additional compensation: Annual Bonus and RSUs Locations: NYC, San Francisco, or Dallas Contact: Nicholas.david@forsythbarnes.com Key Responsibilities Build, validate, and maintain ML/AI models that drive improvements across the value chain (e.g., demand forecasting, inventory optimization, dynamic replenishment, transportation efficiency). Develop algorithms and automated processes that integrate, cleanse, and evaluate large datasets from diverse operational systems — including procurement, manufacturing, logistics, and sales. Design and deploy optimization frameworks to simulate supply scenarios, evaluate trade-offs (cost, speed, sustainability), and recommend data-driven decisions. Collaborate with cross-functional teams in operations, planning, sourcing, and technology to develop scalable data pipelines and modeling workflows. Communicate actionable insights from large and complex datasets to business stakeholders, influencing strategic planning and execution. Provide technical leadership on model architecture, data science best practices, and AI governance. Set strategy and priorities for the data science function supporting value chain analytics, including planning and resource allocation. Qualifications Direct experience building predictive or optimization models for supply chain, value chain, or manufacturing operations. Advanced proficiency in Python, R, Spark, Hive, and SQL for large-scale data manipulation in on-prem and cloud environments (e.g., Azure, AWS, GCP). Strong knowledge of machine learning and statistical modeling techniques, including regression, time-series forecasting, boosted trees, neural networks, and reinforcement learning. Familiarity with optimization and simulation methods such as mixed-integer programming, heuristics, or Monte Carlo simulation. Proven ability to translate analytical findings into operational actions and measurable business impact. Strong collaboration and communication skills; able to work effectively with engineering, operations, and executive stakeholders. Experience leading data-driven initiatives that influence supply chain or value chain transformation. Employment details Seniority level: Mid-Senior level Employment type: Full-time Job function: Information Technology Industry: Retail

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