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Model Predictive Control Jobs in Georgia (NOW HIRING)

Experience applying predictive modeling techniques to pricing, risk, product, or other complex business applications. * Experience with Git, GitLab, or other version control and collaborative ...

Introduce advanced analytics, predictive modeling, and AI tools into cash forecasting and reporting ... strict payment control standards. * Global Liquidity Structures: Optimize and manage global ...

Introduce advanced analytics, predictive modeling, and AI tools into cash forecasting and reporting ... strict payment control standards. * Global Liquidity Structures: Optimize and manage global ...

Introduce advanced analytics, predictive modeling, and AI tools into cash forecasting and reporting ... strict payment control standards. * Global Liquidity Structures: Optimize and manage global ...

Staff Reliability Engineer

Atlanta, GA · On-site

$98K - $124K/yr

Perform predictive reliability analysis to calculate probability of loss of control, loss of asset ... Develop Maintainability prediction models using MIL‑HDBK‑472, and support the development of ...

Showing results 21-40

Model Predictive Control information

What is model predictive control?

Model Predictive Control (MPC) is an advanced method of process control that uses a mathematical model to predict and optimize the future behavior of a system. It works by solving an optimization problem at each control step to determine the best sequence of control actions, taking into account system constraints and objectives. MPC is widely used in industries such as chemical processing, energy, and automotive because it can handle multivariable control problems and anticipate future events. Its predictive nature allows for improved performance, stability, and efficiency compared to traditional control methods.

What are the typical challenges faced by engineers working with model predictive control systems in an industrial setting?

Engineers working with Model Predictive Control systems often encounter challenges related to model accuracy, computational demands, and real-time implementation. Ensuring the process model accurately represents the plant dynamics is critical, as discrepancies can lead to suboptimal control performance. Additionally, MPC algorithms can be computationally intensive, particularly for large-scale or fast processes, requiring careful tuning and optimization to maintain real-time operation. Collaboration with process engineers and IT specialists is common, as integrating MPC with existing control systems and plant infrastructure is a key part of the role.

What are the key skills and qualifications needed to thrive as a model predictive control engineer, and why are they important?

To thrive as a Model Predictive Control Engineer, you need strong foundations in control theory, applied mathematics, and process engineering, usually supported by a degree in engineering or a related field. Proficiency with simulation tools such as MATLAB/Simulink, programming languages like Python or C++, and familiarity with industrial automation systems are typically required. Analytical thinking, problem-solving abilities, and effective communication skills help distinguish top performers in this role. These skills are essential for designing, implementing, and optimizing advanced control algorithms that improve system performance and reliability in complex industrial environments.

What is the difference between Model Predictive Control vs Control Systems Engineer?

AspectModel Predictive ControlControl Systems Engineer
CredentialsEngineering degree, control theory, process modelingEngineering degree, control systems, automation
Work EnvironmentIndustrial automation, process control, manufacturingDesign, develop, and maintain control systems across industries
Industry UsageProcess industries, chemical, oil & gas, manufacturingAutomation, robotics, embedded systems, industrial sectors

Model Predictive Control (MPC) focuses on advanced control algorithms for optimizing processes, while Control Systems Engineers design and implement various control systems. MPC is a specialized skill within control engineering, often requiring knowledge of process modeling and optimization, whereas Control Systems Engineers have broader responsibilities across multiple control technologies. Both roles are essential in industrial automation but differ in scope and application.

What does a model predictive control do?

A Model Predictive Control (MPC) engineer designs control systems that use a mathematical model to predict future system behavior and optimize control actions accordingly. MPC is commonly used in industries like process control and robotics, requiring skills in control theory, programming, and system modeling. The role involves developing algorithms, tuning controllers, and ensuring system stability and efficiency.

What job categories do people searching Model Predictive Control jobs in Georgia look for?

The top searched job categories for Model Predictive Control jobs in Georgia are:

What cities in Georgia are hiring for Model Predictive Control jobs?

Cities in Georgia with the most Model Predictive Control job openings:

Infographic showing various Model Predictive Control job openings in Georgia as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 18% Part Time, 2% Contract, and 1% Nights. Highlights an 95% Physical, 1% Hybrid, and 4% Remote job distribution.

Sr Data Scientist (APM)

Atlanta, GA

Full-time

Medical, Life

Re-posted 20 days ago


Job description

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.

Responsibilities & Qualifications

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.

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

Location Profile

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.

Disclaimer

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 - Novelis2. Verify any communication with us by contacting our talent team at Careers - Novelis

Employment Type: FULL_TIME