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Model Predictive Control Jobs in Jackson, MS (NOW HIRING)

innovitusa

Data Scientist

Jackson, MS · On-site

Develop predictive models and machine learning algorithms to solve business challenges. * Perform ... Experience with version control using Git.

CBRE

Talent Community Hinds Community College

Brandon, MS · On-site

$18.25 - $22.25/hr

... Predictive Maintenance (PdM) tools, and your maintenance knowledge to supervise and resolve ... Install, maintain, and solve relay logic, ladder diagrams, control components, photo-eyes, motor ...

All JobsModel Predictive Control JobsModel Predictive Control Jobs in Jackson, MS

Model Predictive Control information

See Jackson, MS salary details

$47.9K

$84.2K

$114.2K

How much do model predictive control jobs pay per year?

As of Jul 27, 2026, the average yearly pay for model predictive control in Jackson, MS is $84,157.00, according to ZipRecruiter salary data. Most workers in this role earn between $72,800.00 and $94,100.00 per year, depending on experience, location, and employer.

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 Jackson, MS? For Model Predictive Control jobs in Jackson, MS, the most frequently searched job titles are:
What job categories do people searching Model Predictive Control jobs in Jackson, MS look for? The top searched job categories for Model Predictive Control jobs in Jackson, MS are:
Model Predictive Control jobs near you
Infographic showing various Model Predictive Control job openings in Jackson, MS as of July 2026, with employment types broken down into 1% As Needed, 75% Full Time, 19% Part Time, 1% Temporary, 3% Contract, and 1% Nights. Highlights an 96% Physical, 1% Hybrid, and 3% Remote job distribution, with an average salary of $84,157 per year, or $40.5 per hour.
Data Scientist

Data Scientist

innovitusa

Jackson, MS • On-site

Contractor

This job post has expired today. Applications are no longer accepted.

Job description

We are seeking a highly analytical Data Scientist to leverage data, statistical methods, and machine learning techniques to solve complex business problems. The ideal candidate will collect, analyze, and interpret large datasets, build predictive models, and communicate actionable insights to stakeholders. You will collaborate with engineering, product, business, and leadership teams to drive data-informed decision-making.

Key Responsibilities
  • Collect, clean, and analyze structured and unstructured datasets.
  • Develop predictive models and machine learning algorithms to solve business challenges.
  • Perform exploratory data analysis (EDA) to identify trends, patterns, and anomalies.
  • Design, implement, and evaluate statistical models and experiments.
  • Build data pipelines and automate data processing workflows.
  • Develop dashboards, reports, and visualizations to communicate insights.
  • Collaborate with data engineers, software developers, product managers, and business stakeholders.
  • Validate model performance and continuously improve model accuracy.
  • Deploy machine learning models into production environments.
  • Document methodologies, findings, and technical solutions.
  • Stay current with emerging trends in AI, machine learning, and data science.
  • Participate in Agile ceremonies and contribute to cross-functional initiatives.
Required Qualifications
  • Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
  • 8+ years of experience in data science, machine learning, or advanced analytics.
  • Strong proficiency in Python or R.
  • Experience with SQL and database querying.
  • Solid understanding of statistics, probability, and machine learning algorithms.
  • Experience with data visualization tools such as Tableau, Power BI, or Matplotlib.
  • Knowledge of data preprocessing, feature engineering, and model evaluation.
  • Familiarity with cloud platforms (AWS, Azure, or Google Cloud Platform).
  • Strong analytical, problem-solving, and communication skills.
Preferred Qualifications
  • Master's or Ph.D. in a quantitative discipline.
  • Experience with deep learning frameworks such as TensorFlow or PyTorch.
  • Knowledge of NLP (Natural Language Processing), Computer Vision, or Generative AI.
  • Experience deploying ML models using Docker and Kubernetes.
  • Familiarity with MLOps tools such as MLflow, Kubeflow, or SageMaker.
  • Experience working with big data technologies such as Spark or Hadoop.
  • Knowledge of A/B testing and experimental design.
  • Experience with version control using Git.