Job description

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 Senior Data Scientist, (APM), supports the design, development, and deployment of data science and machine learning solutions that improve asset reliability, reduce unplanned downtime, and strengthen maintenance decision-making across Novelis’ manufacturing operations. Reporting to the Sr AI Engineer Leader of APM, this role partners with Operations, Reliability, Data Engineering, and AI Governance to translate industrial data into practical, actionable insights. The role develops and supports failure-prediction models, equipment health-monitoring logic, anomaly detection, and remaining-useful-life estimators for human decision support—not autonomous control. This is a hands-on senior data science role with a reliability focus. The Senior Data Scientist helps convert prioritized APM use cases into reliable production solutions by combining statistical analysis, machine learning, time-series modeling, sensor data interpretation, and practical understanding of maintenance and reliability workflows. The role works within the technical direction, roadmap, and architecture established by the Sr AI Engineer Leader of APM while maintaining model quality, operational usability, and trust with plant stakeholders. Capability Alignment This role is aligned to the APM delivery team within the Decision Intelligence & AI Enablement pillar and contributes to the following enterprise capabilities: • Industrial Data Science for Predictive Maintenance and Asset Reliability • Failure Prediction, Remaining Useful Life Modeling, and Reliability Analytics • Equipment Health Monitoring, Anomaly Detection, and Alert Quality Improvement • Time-Series Modeling, Sensor Data Analysis, and Operational Context Interpretation • Model Lifecycle Management for Industrial Analytics and Production Data Science • Responsible AI Compliance in Operational Environments, aligned to AI Governance standards Responsibilities Data Science Development & Reliability Analytics • Develop and deliver data science components of predictive maintenance and asset-reliability systems, including data preparation, feature engineering, exploratory analysis, model development, deployment support, and monitoring workflows. • Build, validate, and improve production-grade failure prediction models, equipment health scores, remaining-useful-life estimators, and anomaly detection methods that produce useful recommendations for maintenance and operations teams. • Apply statistical analysis, machine learning, time-series modeling, and reliability engineering judgment to solve industrial monitoring problems using appropriate evaluation methods and deployment patterns. • Analyze sensor, historian, maintenance, and operational data to identify asset behavior, failure patterns, signal quality issues, model drift, and opportunities to improve alert precision and credibility. • Support model lifecycle management through monitoring, retraining support, documentation, version control, testing, validation, and production troubleshooting. Execution Alignment & Cross-Functional Delivery • Deliver assigned APM work in alignment with Novelis’ enterprise data and reliability priorities, including trusted data, operational reliability, metal flow optimization, sustainability goals, and operational efficiency. • Work with reliability, operations, automation, information technology, and data engineering stakeholders to connect analytical findings to practical maintenance decisions and sustainable production use. • Support feature scoping, sprint execution, testing, deployment, user adoption, and continuous improvement activities aligned to the APM delivery roadmap and critical metric framework. Accountability Boundaries This role delivers data science models, analyses, and engineering components within the roadmap, technical architecture, and technology direction owned by the Sr AI Engineer Leader of APM. It supports predictions and recommendations for maintenance and operations teams; people in Operations and Reliability make and complete the maintenance decisions and actions. This role contributes to the APM roadmap, model standards, analytics validation, production monitoring, and stakeholder feedback loops, but does not own enterprise architecture, autonomous closed-loop control, AI governance standards, core data platforms, business target definitions, data governance rules, master data policy, or data access configuration. Where a use case warrants autonomous closed-loop execution rather than human action, this role supports handoff to AI Automation for engineering and runtime ownership. Minimum Qualifications • Bachelor’s degree in Data Science, Computer Science, Engineering, Statistics, Applied Mathematics, Reliability Engineering, or a related field. • Minimum of 3 years of experience in data science, machine learning, reliability analytics, predictive maintenance, industrial analytics, or related applied analytics work. • Experience developing analytical or predictive models using time-series data, sensor data, equipment telemetry, maintenance records, or manufacturing process data. • Proficiency in Python, SQL, and common data science or machine learning libraries; ability to investigate data quality issues and explain model outputs to technical and non-technical stakeholders. • Strong analytical, communication, and problem-solving skills, with interest in manufacturing, maintenance, reliability, or industrial decision support. • Going forward, employees based in Atlanta will be expected to work in the office Monday through Thursday, with Friday available to work remotely or from the office. Preferred Qualifications • Master’s degree or advanced certification in Data Science, Machine Learning, Statistics, Engineering, Reliability, or a related field. • Experience in manufacturing, industrial operations, reliability engineering, maintenance analytics, or asset performance management. • Familiarity with industrial historians, condition monitoring data, edge or cloud analytics environments, Databricks, Power BI, Azure, or production model deployment practices. • Experience translating analytical outputs into maintenance, reliability, or operational actions in partnership with plant stakeholders. • Familiarity with production data science, model deployment, or analytics platform practices, including tools such as Docker, Kubernetes, Azure, Databricks, or similar cloud and containerized deployment environments. Please note that we are unable to provide visa sponsorship for this position. Candidates must be legally authorized to work in the United States without the need for current or future sponsorship What We Offer: Novelis' benefits say a lot about how we care for each other. Our employees and their families have many different needs. As a result, our benefits offer choices on many levels and are high in quality, driven by the marketplace, and affordable. In addition to core benefits, we provide these unique to the industry benefits: • Family Growth Programs: Paid parental Leave, Adoption Assistance, Fertility Treatment, Childcare Discount and Nursing Mom Support • Employee Assistance Programs: free resources available 24/7 to you and your family in the areas of mental health, family life, and career and financial guidance • Wellness Programs: incentives for wellness activities, wellness spending account, programs for building healthy habits, virtual physical therapy for joint, back, and pelvic health, health management programs and more. • Diabetes Management Program • Pet insurance • Identity Theft Protection • PerkSpot Discount Program • Tuition assistance and career development programs! #LI- AC1 #LI- Hybrid Novelis’ Global Corporate and North America Headquarters is located in the Buckhead neighborhood of Atlanta GA employing around 700 people. Supporting it’s 31 operations worldwide Novelis’ corporate office is home to the executive leadership team and global functions that support the automotive beverage can and high-end specialties value streams. The City of Atlanta provides a diverse and family-friendly place to live with countless museums cultural organizations and educational institutions including the Georgia Aquarium Woodruff Arts Center CNN Center Georgia Tech and Mercedes-Benz Stadium. In the Atlanta area Novelis has strong community partnerships with Atlanta Habitat for Humanity GeorgiaFIRST and Agape Youth and Family Center in addition to many local museums and community groups. Novelis recognizes its talented and diverse workforce as a key competitive advantage. Novelis provides equal employment opportunities to all employees and applicants.All terms and conditions of employment at Novelis including recruiting hiring placement promotion termination layoffs recalls transfers leaves of absence compensation and training are without regard to race color religion age sex national origin disability status genetics protected veteran status sexual orientation gender identity or expression or any other characteristic protected by federal provincial or local laws. We encourage all potential candidates to follow the protocols below and to be diligent when sharing any personal information: 1. Check the job posting is live and valid via our careers page: Careers - Novelis 2. Verify any communication with us by contacting our talent team at Careers - Novel is We can recommend jobs specifically for you! Click here to get started.

Requirements

Must-haves

Capability Alignment
This role is aligned to the APM delivery team within the Decision Intelligence & AI Enablement pillar and contributes to the following enterprise capabilities:
Industrial Data Science for Predictive Maintenance and Asset Reliability
Failure Prediction, Remaining Useful Life Modeling, and Reliability Analytics
Equipment Health Monitoring, Anomaly Detection, and Alert Quality Improvement
Time-Series Modeling, Sensor Data Analysis, and Operational Context Interpretation
Model Lifecycle Management for Industrial Analytics and Production Data Science
Responsible AI Compliance in Operational Environments, aligned to AI Governance standards
Data Science Development & Reliability Analytics
Apply statistical analysis, machine learning, time-series modeling, and reliability engineering judgment to solve industrial monitoring problems using appropriate evaluation methods and deployment patterns.
Analyze sensor, historian, maintenance, and operational data to identify asset behavior, failure patterns, signal quality issues, model drift, and opportunities to improve alert precision and credibility.
Support model lifecycle management through monitoring, retraining support, documentation, version control, testing, validation, and production troubleshooting.
Execution Alignment & Cross-Functional Delivery
Work with reliability, operations, automation, information technology, and data engineering stakeholders to connect analytical findings to practical maintenance decisions and sustainable production use.
Support feature scoping, sprint execution, testing, deployment, user adoption, and continuous improvement activities aligned to the APM delivery roadmap and critical metric framework.
Accountability Boundaries
Bachelor’s degree in Data Science, Computer Science, Engineering, Statistics, Applied Mathematics, Reliability Engineering, or a related field.
Minimum of 3 years of experience in data science, machine learning, reliability analytics, predictive maintenance, industrial analytics, or related applied analytics work.

Nice-to-haves

Master’s degree or advanced certification in Data Science, Machine Learning, Statistics, Engineering, Reliability, or a related field.
Experience in manufacturing, industrial operations, reliability engineering, maintenance analytics, or asset performance management.
Familiarity with industrial historians, condition monitoring data, edge or cloud analytics environments, Databricks, Power BI, Azure, or production model deployment practices.
Experience translating analytical outputs into maintenance, reliability, or operational actions in partnership with plant stakeholders.
Familiarity with production data science, model deployment, or analytics platform practices, including tools such as Docker, Kubernetes, Azure, Databricks, or similar cloud and containerized deployment environments.
Please note that we are unable to provide visa sponsorship for this position. Candidates must be legally authorized to work in the United States without the need for current or future sponsorship

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaipythonsqlazuredockerkubernetes

Platform: icims
Effort: medium

Benefits

Benefits detected in the listing.

Parental Leave

Wellness