Data Scientist

Basic Qualifications:

• A Bachelor's degree from an accredited institution in a STEM field.
• Professional data science experience OR a Master's degree in STEM field.
• 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.
• This position entails access to export-controlled items and employment offers are conditioned upon an applicant's ability to lawfully obtain access to such items

Preferred Qualifications:

• 2 or more years of professional data science experience
• Demonstrated success applying advanced statistical methods and/or machine learning algorithms to production/field data using Python or R
• Manufacturing / industrial plant experience is preferred
• 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 analytics 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, Engines currently seeks a Data Scientist to join our team at our Howmet Research Center!

The position is located in Whitehall, Michigan and involves working in a close team environment performing a combination of development and support for Howmet's casting, core, and rings facilities.

Primary Responsibilities:

• Constructing and manipulating large datasets using tools like SAS, SQL, Python, R, MiniTab, PowerBI, and more to extract multi-factor interactions and drive change.
• Solving manufacturing, quality, and engineering problems using data analytics and science tools.
• Identifying opportunities and deploying tools to drive continuous improvement through data analytics and science
• 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 predictive models using big data.