Metallurgical Engineer/ICME

Company Description Kaiser Aluminum is a leading producer of semi-fabricated aluminum mill products engineered for strength, quality, and recyclability, supporting a safer and more sustainable world. Its products serve critical markets including food and beverage packaging, automotive applications that improve vehicle efficiency and safety, and aerospace components for both commercial and military aircraft. The company’s culture is grounded in safety, innovation, sustainability, teamwork, and integrity, and it emphasizes customer-focused, innovative solutions. Kaiser Aluminum attributes its success to a diverse workforce of technical, operational, and business professionals working toward shared goals. With more than 75 years of history, the company offers long-term career opportunities for individuals who want to innovate and help shape the future of the industry.

Role Description The Metallurgical Engineer is a full-time, on-site role based in Spokane, WA. This role is responsible for supporting production operations by:

• Conducting low cost and high quality product and process improvement and optimization through the combination of advanced data analytics and ICME (Integrated Computational Material Science).
• Monitor, evaluate, and apply the advanced computing technologies and algorithms for the most sophistic data analytics to uncover insights and hidden variables, identify root causes and patterns, forecast outcomes, and provide data-drive solutions.
• Production process data collection, high dimension large dataset multivariate analysis, classic and penalized regression predictive modelling, advanced neural-network and decision-tree based predictive modeling development and applications.
• Develop general and challenge specific predictive modeling capabilities by using commercially available software and/or internal computational codes.
• Design and development of alloys and processes for Aluminum products through advanced data analytics and ICME.
• Provide guidance and mentorship supports for different levels of data analytics including the principle and limits of different analytical algorithms.
• Provide support for Industry 4.0 initiatives.
• Develop, define, and complete on-time assigned research & development chartered projects.
• Support plant projects as directed.
• Write technical reports summarizing work as well as presentations to peers on progress at continuous improvement meetings.
• Perform metallurgical and structural analysis of aluminum products.
• Work in cross-functional teams to develop process and production improvements.
• 5S of assigned areas.

Qualifications

• MS degree in Data Science, or Computational Material Science, or Statistics, or Material Science or Mechanical Engineering is required. A PhD is preferred.
• Excellent experience in statistical analysis and computing for data-driven problem solving in manufacturing environment.
• Strong skills and experience in predictive modeling development by using multivariate analysis, classic and penalized regression predictive modelling, advanced neural-network and decision-tree based predictive modeling technologies.
• Experience with the advanced computing technologies and algorithms, such as machine learning, to build high quality predictive modes, uncover insights and hidden variables, and provide solutions.
• Experience in large dataset collection, integration, and visualization by using Excel, SQL, Mintab, Python, etc.
• Background in aluminum manufacturing, physical, and mechanical metallurgy.
• Experience in thermodynamic and kinetic material modeling by using CALPHA principle, and process simulations by using FEA and CFD tools.
• An ability to apply continuous improvement tools and methodologies to processes and projects
• Excellent communication skills (written and verbal) and organizational skills.
• Proven ability to work in teams.
• Ability to manage multiple tasks simultaneously.

PREFERRED SKILLS

• Experience in metal rolling process simulation through FEA, Crystal Plasticity (CP) model, and other microstructure-based material models.
• Experience in material characterization such as microstructure and mechanical properties.

WORK ENVIRONMENT

• Approximately 70% of the job function is performed in an office setting requiring normal safety precautions. 30% of the job function is required in the plant. There is exposure to operating machinery and a manufacturing environment.