Senior Data Scientist- Supply Chain

About CooperSurgical

CooperSurgical is a leading fertility and women’s healthcare company dedicated to putting time on the side of women, babies, and families at the healthcare moments that matter most in life. As a division of CooperCompanies, we're driven by a unified purpose to enable patients to experience life's beautiful moments. Guided by our shared values – dedicated, innovative, friendly, partners, and do the right thing – our offerings support patients throughout their lifetimes, from contraception to fertility and birth solutions, to women’s and family care, and beyond. We currently offer over 600 clinically relevant medical devices to healthcare providers, including testing and treatment options, as well as an innovative suite of assisted reproductive technology and genomic testing solutions. Learn more at www.coopersurgical.com. 

Work location: Trumbull, CT or Livingston, NJ (on-site)

Scope: 

The Senior Data Scientist – Supply Chain is responsible for designing, operating, and continuously improving enterprise-scale demand forecasting models that directly support global supply chain planning, inventory optimization, and operational decision-making. The role impacts annual revenue planning and inventory investment decisions across multiple product lines and regions, influencing service levels, working capital, and operational efficiency.

This position works cross-functionally with Supply Chain, Operations, Demand Planning, Finance, IT, and Analytics teams across regions and manufacturing or distribution sites. External interaction may include technology vendors, data platform providers, and consulting partners related to forecasting tools, cloud platforms, and ERP integrations.

Job Summary:

The Senior Data Scientist – Supply Chain exists to own, enhance, and scale advanced demand forecasting capabilities that support supply chain operations and strategic planning. This role is responsible for running a large, complex forecasting model, improving its accuracy and robustness, and leading the automation of the end-to-end forecasting pipeline within the Snowflake data platform.

The position combines deep technical expertise in Python, machine learning, and data engineering with strong supply chain domain knowledge. The role requires the ability to work effectively with incomplete, messy, and evolving data while partnering closely with non-technical stakeholders to translate analytical insights into operational impact.

• Own and operate enterprise demand forecasting models
• Designs, runs, monitors, and continuously improves large-scale demand forecasting models supporting supply chain and operations; accountable for forecast accuracy, stability, and timely delivery to planning stakeholders.
• Automate and scale forecasting pipelines in Snowflake
• Leads the design and implementation of automated, production-ready forecasting workflows within the Snowflake data platform using Python, SQL, and notebooks; accountable for reducing manual effort, improving repeatability, and ensuring data lineage and reliability.
• Develop and maintain advanced analytical codebases
• Writes, reviews, and maintains advanced Python (and/or R) scripts and notebooks for data preparation, feature engineering, model training, validation, and deployment; accountable for code quality, performance, and documentation.
• Partner with Supply Chain and Operations stakeholders
• Works closely with Demand Planning, Supply Chain, Operations, and Finance teams to understand business requirements, translate them into analytical solutions, and explain model outputs and limitations; accountable for stakeholder alignment and adoption of insights.
• As business needs dictate, works extended hours to complete daily department goals or tasks to include mandatory overtime.
• Manage and analyze complex, messy data environments
• Integrates data from multiple sources including ERP systems, Snowflake tables, and external datasets; accountable for data quality assessment, anomaly detection, and pragmatic solutions in ambiguous or incomplete data scenarios.
• Leverage AI tools to accelerate data science productivity
• Effectively utilizes AI tool