Data Science Manager

As a Data Science Manager you will set the vision and lead the development of a highperforming analytics this role you will guide a dedicated group of data scientists partner with key collaborators and deliver data-driven solutions that address complex strategic and operational challenges. You will use strong problemsolving and analytical skills to uncover insights improve processes and create measurable business impact. This is a highly collaborative role working closely with operations engineering IT and leadership teams across the organization.

Responsibilities:

• 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 datadriven solutions for complex business challenges and supports both tactical and long-term objectives.

• Collaborate with crossfunctional 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 highperformance 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.

Minimum Qualifications:

• 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 decisionmaking.

• 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 crossfunctional 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 adhoc work through training enablement and sustainable productization.

Preferred Qualifications:

• Experience supporting yield ramp and HVM enablement including finding opportunities investigating yieldlimiting mechanisms and scaling solutions from rapid tactical fixes to longterm 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.