Minimum 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 experience in advanced data science or machine learning required. Demonstrated success applying advanced statistical methods and machine learning algorithms to production/field data using Python or R. ​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:

5+ years of professional data science or machine learning experience. Manufacturing / industrial plant experience  Experience in applying advanced data and statistical analysis methods to industrial manufacturing data. Visualization tools: Power BI, Tableau Engineering data tools: SQL, SAS, Minitab, JMP, MS Excel, Six Sigma In depth knowledge of advanced analytic and machine learning techniques. Strong verbal and written communication skills.  Excellent analytical skills. Ability to work in a self-directed AND cross-functional team environment. Strong organizational skills.

Howmet Aerospace is hiring a Machine Learning Engineer for our Research and Development group. This position involves working in a close cross-functional team environment supporting Howmet’s casting, alloy, core, and rings facilities. 

Primary Responsibilities:

Evaluating, developing, and testing Artificial Intelligence / Machine Learning applications and solutions. Developing and optimizing machine learning algorithms for Howmet products across all businesses. Constructing and manipulating large datasets using tools such as Python, R, SAS, SQL, Minitab, PowerBI and more, to extract multi-factor and complex interactions to drive improvements in manufacturing. Solving operational, quality, and engineering problems using big data and image sets. Identifying opportunities and deploying tools to drive continuous improvement using machine learning. Driving a data-driven culture across the organization through expanding applications and training.  Interacting with internal customers to drive validation trials, implement process improvements, and integrate machine learning into current production.