Senior Data Scientist

Senior Data Scientist Non-Quality Costs (NQC) represent a significant opportunity for value creation across client’s manufacturing network. This role exists to partner closely with business stakeholders to identify key areas where AI and machine learning can reduce NQC by improving visibility, decision-making, and operational efficiency. The Senior Data Scientist will work closely with Plant Operations, Quality, and Engineering teams to uncover root causes, predict quality risks, and support data-driven process improvements. The role will evolve beyond predictive modeling toward prescriptive recommendations, requiring close collaboration with plant stakeholders to ensure optimization outputs are practical, safe, and compatible with operational constraints. Key Responsibilities Collaborate with business stakeholders (Shift Managers, Operations, Plant Leadership, Finance) to identify and prioritize key drivers of NQC and opportunities for AI/ML impact. Engage deeply with the business to understand current KPIs, data availability, and ways of working to ensure solutions are practical and aligned with operational realities. Analyze manufacturing, quality, and process data to identify key drivers of NQC. Perform advanced exploratory and multivariate analyses to uncover root causes of variability. Build predictive models linking process parameters to quality outcomes (defects, scrap, rework). Build decision-support tools that translate predictive insights into actionable, plant-ready operating recommendations. Develop explainable models to support operational trust and adoption. Translate analytical insights into clear, actionable recommendations for plant teams. Partner with Quality, Engineering, and Continuous Improvement teams to co-design solutions. Support the transition from diagnostic to predictive and prescriptive analytics. Collaborate with Product Managers and Data Engineers to deploy scalable solutions. Validate model performance and monitor impact over time. Review and elevate analytical work produced by other data scientists. Context and Scope This role operates in complex, real-world manufacturing environments characterized by imperfect data, legacy systems, and strong local ownership. Success depends on the ability to build trust, communicate clearly, and balance analytical rigor with operational pragmatism. The role is critical in enabling associates at factories to meet NQC goals through data-driven insights and decision support. Key Requirements: Strong experience in NQC (Non-Quality Cost), manufacturing quality, and root-cause analysis Strong Python and SQL skills. Experience working with large, messy operational datasets. Strong experience in: Root cause analysis using data Multivariate process analysis Anomaly and defect detection Familiarity with industrial statistics: Statistical Process Control (SPC) charts Design of Experiments (applied) Experience with supervised learning for quality prediction. Experience with mathematical optimization methods (e.g., linear programming, mixed-integer programming, stochastic/robust optimization). Ability to integrate optimization models with predictive models to create true end-to-end prescriptive solutions. Cloud experience (Azure preferred). Saxon Global