Data Science Specialist

As an experienced Data Science Lead, you will be responsible for driving advanced analytics, AI/ML initiatives, and data-driven transformation programs to deliver measurable business impact across manufacturing, operations, and supply chain functions. - Lead the design and delivery of advanced analytics and machine learning solutions focused on manufacturing productivity improvement, process optimization, quality, yield, downtime, cost optimization, predictive analytics, and simulation-based analytics. - Own the end-to-end analytics lifecycle, including problem definition, data exploration, model development, validation, deployment, and performance monitoring. - Develop and review machine learning and statistical models using Python and SQL. - Collaborate with data engineering and platform teams to productionize analytics solutions, ensuring model robustness, explainability, and business relevance. - Lead, mentor, and develop a team of Data Scientists and Analysts, establishing best practices, reusable frameworks, and technical standards. - Partner with global business stakeholders to translate challenges into structured analytics opportunities, present insights and recommendations to senior leadership, and act as a key bridge between business objectives and analytics execution. Required Skills & Experience: - Strong hands-on expertise in Python (Pandas, NumPy, Scikit-Learn, Statsmodels, etc.) and advanced SQL skills. - Experience with Azure Cloud, Azure Data Factory (ADF), and Snowflake. - Strong understanding of machine learning, statistical modeling, optimization techniques, forecasting, time-series analytics, and operational analytics. - Experience deploying enterprise-scale analytics solutions from PoC to production. - Experience in Manufacturing, Industrial Analytics, Operations, Productivity Improvement, or Supply Chain Analytics. - Proven leadership experience in Data Science or Analytics teams with strong communication, stakeholder management, and problem-solving capabilities. - Bachelor's or Master's degree in Engineering, Computer Science, Statistics, Mathematics, Data Science, or a related field. - 814+ years of overall experience in Data Science, Analytics, or AI/ML, with 35+ years of experience leading Data Science or Advanced Analytics teams. - Experience working within GCC, CoE, or global delivery environments is preferred. As an experienced Data Science Lead, you will be responsible for driving advanced analytics, AI/ML initiatives, and data-driven transformation programs to deliver measurable business impact across manufacturing, operations, and supply chain functions. - Lead the design and delivery of advanced analytics and machine learning solutions focused on manufacturing productivity improvement, process optimization, quality, yield, downtime, cost optimization, predictive analytics, and simulation-based analytics. - Own the end-to-end analytics lifecycle, including problem definition, data exploration, model development, validation, deployment, and performance monitoring. - Develop and review machine learning and statistical models using Python and SQL. - Collaborate with data engineering and platform teams to productionize analytics solutions, ensuring model robustness, explainability, and business relevance. - Lead, mentor, and develop a team of Data Scientists and Analysts, establishing best practices, reusable frameworks, and technical standards. - Partner with global business stakeholders to translate challenges into structured analytics opportunities, present insights and recommendations to senior leadership, and act as a key bridge between business objectives and analytics execution. Required Skills & Experience: - Strong hands-on expertise in Python (Pandas, NumPy, Scikit-Learn, Statsmodels, etc.) and advanced SQL skills. - Experience with Azure Cloud, Azure Data Factory (ADF), and Snowflake. - Strong understanding of machine learning, statistical modeling, optimization techniques, forecasting, time-series analytics, and operational analytics. - Experience deploying enterprise-scale analytics solutions from PoC to production. - Experience in Manufacturing, Industrial Analytics, Operations, Productivity Improvement, or Supply Chain Analytics. - Proven leadership experience in Data Science or Analytics teams with strong communication, stakeholder management, and problem-solving capabilities. - Bachelor's or Master's degree in Engineering, Computer Science, Statistics, Mathematics, Data Science, or a related field. - 814+ years of overall experience in Data Science, Analytics, or AI/ML, with 35+ years of experience leading Data Science or Advanced Analytics teams. - Experience working within GCC, CoE, or global delivery environments is preferred.