Sr Data Scientist; APM

Position Overview

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…