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Director, Data Science & Analytics

Coherent UK
Santa Clara, USA
Full-time
Director · 10+ years experience
Hybrid
Discovered 4 days ago
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Full Job Posting

What You Bring

  • Bachelor's degree in statistics, computer science, mathematics, engineering, economics, or a related field.
  • 10+ years in data science or analytics, including 5+ years leading teams of data scientists, analysts, or engineers.
  • Hands-on expertise in Python and SQL, plus machine learning, statistical modeling, and experimental design.
  • A track record of taking models from prototype to production and measuring their business value.
  • Experience with modern data and ML platforms, such as Databricks, Snowflake, Spark, or AWS, Azure, or GCP services.
  • Clear, concise communication with executives and non-technical partners.
  • The ability to rank competing priorities by business impact and say no to the rest.

Nice to Have

  • Master's or PhD in a quantitative field.
  • Experience in semiconductor, photonics, optical networking, or other high-tech manufacturing.
  • Familiarity with manufacturing data, such as MES, SPC, test and yield data, or equipment sensor data.
  • Experience deploying generative AI or LLM solutions in an enterprise setting.
  • Certifications such as AWS Machine Learning, Databricks, PMP, or CIPP.
  • Safety Requirements
  • All employees are required to follow the site EHS procedures and Coherent Corp. Corporate EHS standards.
  • Quality and Environmental Responsibilities
  • Depending on location, this position may be responsible for the execution and maintenance of the ISO 9000, 9001, 14001 and/or other applicable standards that may apply to the relevant roles and responsibilities within the Quality Management System and Environmental Management System.
  • Culture Commitment
  • Ensure adherence to company’s values (ICARE) in all aspects of your position at Coherent Corp.:
  • I ntegrity – Create an Environment of Trust
  • C ollaboration – Innovate Through the Sharing of Ideas
  • A ccountability – Own the Process and the Outcome
  • R espect – Recognize the Value in Everyone
  • E nthusiasm – Find a Sense of Purpose in Work
  • Coherent Corp. is an equal opportunity/affirmative action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law.
  • If you need assistance or an accommodation due to a disability, you may contact us at [email protected].

Qualifications

  • Bachelor's degree in statistics, computer science, mathematics, engineering, economics, or a related field.
  • 10+ years in data science or analytics, including 5+ years leading teams of data scientists, analysts, or engineers.
  • Hands-on expertise in Python and SQL, plus machine learning, statistical modeling, and experimental design.
  • A track record of taking models from prototype to production and measuring their business value.
  • Experience with modern data and ML platforms, such as Databricks, Snowflake, Spark, or AWS, Azure, or GCP services.
  • Clear, concise communication with executives and non-technical partners.
  • The ability to rank competing priorities by business impact and say no to the rest.

Responsibilities

  • Set the strategy. Define and run Coherent's data science and analytics roadmap. Tie every initiative to business goals and track its measurable return.
  • Deliver models that ship. Lead the development and deployment of predictive models, machine learning, and statistical analysis. Priority areas include yield and process optimization, predictive maintenance, demand forecasting, and supply chain planning.
  • Build and lead the team. Hire, develop, and manage data scientists, analysts, and ML and data engineers. Create a culture of rigor, ownership, and collaboration.
  • Own the platform. Guide the data and ML infrastructure, including cloud data platforms, pipelines, MLOps, and BI tools. Make sure it scales across global fabs and sites.
  • Drive business impact. Turn complex analysis into clear recommendations for executives. Partner with operations, engineering, product, finance, and sales to put solutions into daily use.
  • Establish governance. Set data quality, governance, and model-risk standards. Ensure compliance with security, privacy, and export-control requirements.
  • Bring in what's next. Evaluate emerging methods, including generative AI and LLMs. Pilot the ones that prove value and scale them.

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