Data Science Manager

Lead and develop a high-performing Data Science team. Mentor members, manage performance, and ensure timely completion of required training. Encourage a culture of continuous learning and growth. Oversee Data Science applications and global support, defining, implementing, and maintaining Best Known Methods across regions to ensure consistency and operational excellence. Provide strategic leadership that drives innovative, data‑driven solutions for complex business challenges and supports both tactical and long-term objectives. Collaborate with cross‑functional partners to agree on expectations, communicate priorities, and deliver solutions that support key performance metrics and organizational goals. Develop, guide, and review advanced Machine Learning and Deep Learning models using large datasets, sensor data, metadata, and predictive modeling techniques. Select and communicate modeling approaches clearly, including model behavior, design considerations, visualization modes, imaging concepts, and interpretability. Leverage high‑performance computing environments (Spark, OpenShift, CPU/GPU architectures) to enable scalable model development and sophisticated analytics. Lead project governance and reporting, including decisions to modify, sustain, or discontinue initiatives to meet Fab/area objectives, and prepare monthly and quarterly updates while ensuring a safe, ethical, and compliant work environment. MS/PhD (or equivalent experience) in Data Science, Statistics, Computer Science, or an engineering field, with demonstrated experience applying data science to manufacturing, process, yield, or defect data to support decision‑making. Proven ability to deploy analytics and ML solutions that drive yield improvement and process optimization, including anomaly detection, outlier identification, and translating insights into actionable recommendations for process or equipment changes. Strong cross‑functional collaboration skills and the ability to partner with process, equipment, PI, and other domain experts to define problems, validate results, and communicate insights clearly through visualizations or dashboards. Demonstrated success in standardizing and scaling solutions across teams or sites, improving adoption, and reducing ad‑hoc work through training, enablement, and sustainable productization. Experience supporting yield ramp and HVM enablement, including finding opportunities, investigating yield‑limiting mechanisms, and scaling solutions from rapid tactical fixes to long‑term monitoring. Familiarity with yield management platforms and related applications. Experience with virtual metrology, process control, and quality control methods, and building models that integrate with fab workflows to improve detection, stability, and response. Strong data science skills passionate about delivering practical solutions. This includes deploying models and analytics as services or dashboards. Use modern tools like containers, CI-CD, large-scale data pipelines, and visualization or reporting platforms. Proven people-leadership capabilities, including mentoring and developing data scientists. Skilled at managing priorities and working with collaborators. Ensures delivery meets ramp achievements with consistent focus on scope, quality, and timelines.