Machine Learning Engineer at Howmet Aerospace in Whitehall, Michigan

Job Description:

Howmet Aerospace Inc. (NYSE: HWM), headquartered in Pittsburgh, Pennsylvania, is a leading global provider of advanced engineered solutions for the aerospace and transportation industries. Our primary businesses focus on jet engine components, aerospace fastening systems, titanium structural parts and forged wheels. With $6.6 billion in revenue in 2023, our products play a crucial role in enabling fuel efficiency and lightweighting, contributing to our customers' success and making a positive impact on the world. To learn more about the way Howmet Aerospace Inc. is advancing the sustainability of our customers, markets, and communities where we operate, review the 2023 Environmental Social and Governance report at www.howmet.com/esg-report . Follow: LinkedIn , Twitter , Instagram , Facebook , and YouTube .

Equal Opportunity Employer:

Howmet is proud to be an Equal Employment Opportunity and Affirmative Action employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or other applicable legally protected characteristics.

If you need assistance to complete your application due to a disability, please email TalentAcquisitionCoE_Howmet@howmet.comMinimum Qualifications:

• A MS or PhD Degree from an accredited university in Data Science, Computer Science, Computer Engineering, Mathematics, Statistics, Analytics, or related.
• Minimum of 2 years of hands-on experience in deep learning for computer vision.
• Demonstrated success applying advanced statistical methods and machine learning algorithms to production/field data using Python.
• Employees must be legally authorized to work in the United States. Verification of employment eligibility will be required at the time of hire. Visa sponsorship is not available for this position.

Preferred Qualifications:

• Minimum of 5 years of professional experience in data science or machine learning.
• Strong statistical background with demonstrated expertise in analyzing industrial/manufacturing data.
• Familiarity with manufacturing or industrial plant environments and their unique challenges.
• Knowledge of software development life cycle (SDLC)
• Proficiency in visualization tools (e.g., Power BI)
• Comprehensive knowledge of advanced analytics and machine learning techniques.
• Exceptional verbal and written communication skills with the ability to convey complex ideas clearly.
• Strong organizational skills and the ability to work independently and in a cross-functional team environment.

Why Join Us?

• Be at the forefront of innovation, applying machine learning to aerospace manufacturing.
• Collaborate with a supportive, cutting-edge team dedicated to solving challenging real-world problems.
• Access professional growth opportunities and a chance to directly impact Howmet Aerospace's success.

Howmet Aerospace is hiring a Machine Learning Engineer with expertise in deep learning for computer vision to join our innovative Research and Development team. This role involves building cutting-edge computer vision applications and collaborating with cross-functional teams to support our casting, alloy, core, and rings facilities.

Primary Responsibilities:

• Design, develop, and evaluate advanced deep learning architectures (e.g., CNNs, Mask R-CNNs, YOLO) to address complex computer vision challenges.
• Train and optimize generative models to accelerate development.
• Build and refine machine learning algorithms to enhance Howmet products across all business units.
• Construct, manipulate, and analyze large datasets using tools such as Python and SQL.
• Conduct statistical multi-factor analyses to uncover complex relationships and improve manufacturing processes.
• Present data-derived conclusions to a non-technical audience.
• Identify opportunities to optimize processes and implement continuous improvement tools using machine learning.
• Promote a data-driven culture across the organization by expanding machine learning applications and leading training initiatives.
• Collaborate with internal customers to validate trials, implement process enhancements, and integrate machine learning into production workflows.

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