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Data Science Manager

Job in Boise, Ada County, Idaho, 83708, USA
Listing for: Relha LLC
Full Time position
Listed on 2026-02-21
Job specializations:
  • IT/Tech
    Data Science Manager, Data Analyst, AI Engineer, Data Scientist
Salary/Wage Range or Industry Benchmark: 80000 - 100000 USD Yearly USD 80000.00 100000.00 YEAR
Job Description & How to Apply Below

Our vision is to transform how the world uses information to enrich life for all.

Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever.

As a Data Science Manager, you will set the vision and lead the development of a high‑performing analytics team. In 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 problem‑solving 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, 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, Open Shift, 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 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.

Preferred Qualifications:

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…

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