1

Model Predictive Control Jobs in Georgia (NOW HIRING)

... models for water consumption forecasting, anomaly detection, leak detection, and predictive ... control systems (Git) and CI/CD pipelines. • Continued professional self-improvement through ...

... models for water consumption forecasting, anomaly detection, leak detection, and predictive ... control systems (Git) and CI/CD pipelines. • Continued professional self-improvement through ...

The selected candidate will develop dashboards, predictive workforce models, data automation tools ... Experience with Full-Time Support Management Control System (FTSMCS) * Army National Guard, Army ...

You'll collaborate with cross-functional teams to bring predictive models into production and ... control systems such as Git Understanding of software development lifecycle and best practices ...

Senior Data Science Analyst

Atlanta, GA · On-site

$82K - $104K/yr

You'll execute predictive validity studies and outcome analyses that prove ROI for major enterprise ... Apply machine learning models (Random Forest, XGBoost) to predict performance and attrition ...

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 ...

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 ...

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 ...

next page

Showing results 1-20

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 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 are the typical challenges faced by engineers working with Model Predictive Control (MPC) 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 (MPC) 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 are popular job titles related to Model Predictive Control jobs in Georgia? For Model Predictive Control jobs in Georgia, the most frequently searched job titles are:
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 July 2026, with employment types broken down into 95% Full Time, and 5% Contract. Highlights an 100% In-person job distribution.
Senior Data Scientist

Senior Data Scientist

neptune technologies

Duluth, GA • On-site

Full-time

Posted 18 days ago


Job description

Position Summary
As a Senior Data Scientist, you will be responsible for designing and implementing machine learning models and data-driven solutions that enhance our water utility intelligence platform and create value for our customers. This position involves working with large-scale IoT data from millions of water meters, developing predictive analytics capabilities, and deploying AI solutions into production environments. You will collaborate with Product Management and Engineering teams to translate business requirements into data science solutions, mentor junior data scientists, and drive Neptune's AI transformation initiatives. This role provides direct impact on utility operations, water conservation efforts, and customer service improvements.

Responsibilities

• Effectively communicate and articulate decisions, designs, and outcomes to stakeholders at all levels of the organization.
• Work with cross-functional teams to deliver high-quality machine learning models and data science solutions.
• Understand and enhance requirements defined by Product Management for AI-powered features.
• Design and implement machine learning models for water consumption forecasting, anomaly detection, leak detection, and predictive maintenance.
• Develop and deploy production-ready machine learning pipelines on cloud infrastructure (AWS).
• Analyze large-scale time-series data from IoT devices and water utility operations.
• Build and optimize data processing workflows using PySpark and distributed computing frameworks.
• Create data visualizations and analytics dashboards to communicate insights to stakeholders.
• Conduct exploratory data analysis to identify patterns, trends, and opportunities in metering data.
• Perform feature engineering and model selection to optimize predictive performance.
• Evaluate model performance and implement monitoring solutions for production ML systems.
• Collaborate with software engineers to integrate ML models into the Neptune 360 platform.
• Provide technical guidance to Product Management on data science capabilities and feasibility.
• Document data science methodologies, model architectures, and analytical findings.
• Stay current with latest developments in machine learning, AI, and data science best practices.
• Mentor junior data scientists and disseminate technical knowledge within the organization.
• Review code and model implementations of other team members.
• Participate in sprint planning and demonstrate completed work at the end of every iteration.
• Work with Python, SQL, PySpark, AWS services (SageMaker, Bedrock, Lambda, Redshift), and ML frameworks.
• Contribute to Neptune's AI strategy and identify new opportunities for data-driven innovation.

Experience
• 5+ years of experience in data science, machine learning, or related analytical roles.
• 5+ years of experience with Python and data science libraries (pandas, NumPy, scikit-learn, TensorFlow/PyTorch).
• Strong experience with SQL and working with large-scale databases (Redshift, PostgreSQL, MySQL).
• Experience with PySpark and distributed computing frameworks for large-scale data processing, including working with common data formats such as JSON and Parquet.
• Proven track record of deploying machine learning models to production environments.
• Experience with cloud platforms, preferably AWS (SageMaker, Bedrock, Lambda, S3, Redshift).
• Experience with time-series analysis and forecasting methods.
• Understanding of MLOps practices and model lifecycle management.
• Experience building RESTful APIs for model serving.
• Strong statistical analysis and experimental design skills.
• Experience with data visualization tools and techniques.
• Experience working in Agile/iterative development environments.
• Ability to communicate complex technical concepts to non-technical stakeholders.
• Experience with version control systems (Git) and CI/CD pipelines.
• Continued professional self-improvement through courses, certifications, or research.
• Preferred: Experience with AWS big data services (Glue, EMR, Athena).
• Preferred: Experience with IoT data, utility operations, or water management systems.
• Preferred: Experience with generative AI and large language models.

Education
Master's or Ph.D. degree in Data Science, Computer Science, Statistics, Mathematics, or related
quantitative field, or combination of Bachelor's degree with equivalent experience.

Location: Duluth, GA

#HP1