Lead Modeling Scientist

Novelis Global R&T•Kennesaw, GA

About The Position

Novelis is one of the world leaders in aluminum recycling and rolling and a leading sustainable aluminum solutions provider. Driven by our purpose of shaping a sustainable world together, we work alongside our customers to provide innovative solutions to the aerospace, automotive, beverage packaging and specialty markets. Headquartered in Atlanta, Georgia, Novelis has approximately 13,000 employees in 32 operating facilities on 4 continents. The Lead Modeling Scientist has a key role in advancing Novelis’ capabilities in computational modeling, with the primary objective of linking aluminum sheet process conditions, microstructure and texture evolution, and material properties. This work will support faster product innovation across various markets in which Novelis operates, as well as improved plant performance. The role requires an unique combination of deep expertise in metallurgy and materials science, coupled with advanced modeling proficiency. The position focuses on predictive modeling, which is integrated with experimental validation and data analytics to optimize manufacturing processes for aluminum products. As a key member of Novelis’ Americas R&D organization, the Lead Modeling Scientist will spearhead the development and deployment of multi-physics, multi-scale materials modeling and physics-guided AI modeling capabilities. These efforts are critical for accelerating the development of sustainable products and processes. Central to the role is bridging microstructural length scales as applied to Novelis’ products—including sheet and plate—and to the company’s manufacturing processes such as casting, rolling, and heat treatments. The position will also focus on developing a deeper understanding of the relationships among alloy chemistry, thermomechanical processing, microstructure, properties, and overall product performance.

Requirements

Advanced degree (M.S. or Ph.D.) in Materials Science, Mechanical Engineering, or a related field, with more than ten years of experience in computational modeling.
Expertise in metallurgy and materials science, as well as process and microstructure modeling for metallic systems, especially aluminum alloys. Flat rolled aluminum products experience is largely preferred
Proficiency in modeling software such as Thermo-Calc, DICTRA, TC-Prisma, and Pandat, and in numerical methods including finite element, finite difference, cellular automata, and phase-field modeling.
Programming skills in MATLAB, FORTRAN, and C/C++, along with proficiency in statistical analysis tools like JMP and R.
Strong understanding of metallurgical principles and characterization techniques.
Excellent technical communication and project management skills.
Proven track record of solving plant issues through modeling with measurable business impact.
Experience deploying predictive models in production environments to improve throughput and quality.

Nice To Haves

Machine Learning for materials: Property prediction, Process window optimization, Defect classification
Digital twin development: End-to-end process-to-property simulation
High-performance computing (HPC): Parallel simulations, cloud computing
Data pipelines & model deployment: MLOps, version control, model governance
DOE (Design of Experiments)
Multi-variable regression & sensitivity analysis
Model calibration & uncertainty quantification

Responsibilities

Develop and maintain Integrated Computational Materials Engineering (ICME) models that connect process parameters, microstructure, and properties for flat aluminum sheet products, including processes such as homogenization, heat treatment, precipitation, recrystallization, texture development and grain growth.
Design and implement multi-scale models to link process parameters with microstructure and properties for casting, rolling, heat treatment, coating, CASH, batch annealing and related processes relative to Flat Rolled Aluminum Products.
Constitutive behavior models: Flow stress, work hardening, strain rate sensitivity
Formability & failure prediction models: FLD, FLC, earing, spring-back, bendability
Create and refine process simulation tools using methods such as finite element, finite difference, cellular automata, and phase-field modeling to predict thermal and mechanical behavior.
Integrate modeling results with experimental characterization techniques (including SEM, EBSD, XRD, DSC) and plant data to validate predictions and continually improve model accuracy.
Build thermodynamic and kinetic models using tools like Thermo-Calc, DICTRA, TC-Prisma, and Pandat to support alloy design and process optimization.
Lead end-to-end modeling projects, from scoping and model development through validation and deployment, with the goal of improving or innovating aluminum sheet products and processes across beverage packaging, automotive, specialties, and aerospace markets.
Automate workflows for predicting microstructure-property relationships and incorporate feedback from plant trials to enhance model robustness.
Collaborate with plant engineers and R&D teams to apply models for troubleshooting and improving productivity, recovery, and quality.
Document methodologies and results in technical reports and contribute to intellectual property through invention disclosures and patents.

Benefits

Medical, dental and vision insurance
Health savings accounts – Company Funded Health Savings Account (HSA) and Health Reimbursement Account (HRA)
Flexible Spending Account (FSA)
Company-paid basic life insurance and Additional voluntary life coverage
Paid vacation and competitive personal time off
401(k) savings plan with company match
Retiree Medical Plans – medical and prescription drug coverage through Novelis sponsored Retiree Health Access
Retirement Savings Account
Employee assistance programs – available 24/7 to you and your family
Wellness and Work Life Support - career development and educational assistance