About the job

A problem isn’t truly solved until it’s solved for all. That’s why Googlers build products that help create opportunities for everyone, whether down the street or across the globe. As a Technical Program Manager at Google, you’ll use your technical expertise to lead complex, multi-disciplinary projects from start to finish. You’ll work with stakeholders to plan requirements, identify risks, manage project schedules, and communicate clearly with cross-functional partners across the company. You're equally comfortable explaining your team's analyses and recommendations to executives as you are discussing the technical tradeoffs in product development with engineers.

Responsibilities

• Develop and implement data-motivated solutions to improve custom silicon assembly and test manufacturing efficiency, yield, and product quality.

• Utilize statistical analysis and machine learning techniques to analyze large datasets of semiconductor production data, identify trends, and predict potential issues.

• Create automated data pipelines and dashboards to monitor Key Performance Indicators (KPIs) and provide real-time insights to stakeholders.

• Collaborate with cross-functional teams to gather data requirements, develop data analysis strategies, and communicate findings effectively.

Minimum qualifications

• Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, a related technical field, or equivalent practical experience.

• 5 years of experience in program management.

• 5 years of experience in scripting languages, specifically Python, for data manipulation, statistical analysis, and task automation, including building and maintaining automated data pipelines (ETL/ELT) to ensure data quality and availability.

• Experience in overseeing the delivery of data-driven or technical initiatives.

Preferred qualifications

• Master's degree in Computer Science, Data Science, Engineering, Mathematics, or a related technical field.

• 5 years of experience in semiconductor manufacturing and testing.

• Experience in manufacturing, quality, reliability, or product engineering related industries.

• Experience in advanced data visualization tools like Looker, with a focus on creating insights and executive-level dashboards.

• Experience overseeing the AI/ML lifecycle, including the deployment of machine learning models into production and the continuous monitoring of their performance to ensure long-term reliability.

• Familiarity with cloud platforms (e.g., Google Cloud Platform (GCP)) and their native data warehousing solutions.