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Model Predictive Control Jobs in Virginia (NOW HIRING)

@Orchard

Analytic Methodologist - Senior

Reston, VA · On-site

... models, algorithms, and frameworks to improve objectivity, completeness, and predictive value of ... Establish processes for ontology version control, change management, stakeholder and governance ...

@Orchard

Analytic Methodologist - Senior

Reston, VA · On-site

... models, algorithms, and frameworks to improve objectivity, completeness, and predictive value of ... Establish processes for ontology version control, change management, stakeholder and governance ...

M&J Engineering

Data/Business Analyst

Arlington, VA · On-site

Support the creation of data models, predictive analyses, and forecasting tools to enhance planning ... control, teamwork, and the development of younger staff. We seek employees who enjoy their work ...

M&J Engineering

Data/Business Analyst

Arlington, VA · On-site

Support the creation of data models, predictive analyses, and forecasting tools to enhance planning ... control, teamwork, and the development of younger staff. We seek employees who enjoy their work ...

M&J Engineering

Data/Business Analyst

Arlington, VA

Support the creation of data models, predictive analyses, and forecasting tools to enhance planning ... control, teamwork, and the development of younger staff. We seek employees who enjoy their work ...

Vantor

Data Scientist

Reston, VA · On-site

The role involves automating data workflows, analyzing datasets, developing predictive models, and ... version control, testing frameworks, and software engineering best practices. • Interest in ...

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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 Virginia? For Model Predictive Control jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Model Predictive Control jobs in Virginia look for? The top searched job categories for Model Predictive Control jobs in Virginia are:
What cities in Virginia are hiring for Model Predictive Control jobs? Cities in Virginia with the most Model Predictive Control job openings:
Model Predictive Control jobs near you
Infographic showing various Model Predictive Control job openings in Virginia as of July 2026, with employment types broken down into 95% Full Time, and 5% Contract. Highlights an 100% In-person job distribution.
Senior Consultant, Financial Modeling

Senior Consultant, Financial Modeling

FI Consulting

Arlington, VA • On-site

$133K/yr

Full-time

Posted 19 days ago

Job description

FI Consulting is seeking a highly motivated Senior Consultant with Financial Modeling experience to lead client projects, engage with executive stakeholders, and serve as the technical lead for model design and delivery. You will architect modeling approaches, guide analysts and consultants, and take on hands-on build and QA work to meet client objectives in fast-paced, high‑visibility environments.
Responsibilities
  • Lead modeling workstreams: Design and build models: forecasting engines, scenario/sensitivity analyses, and valuation models.
  • Translate analysis to action: Convert model outputs into executive-ready insights and recommendations.
  • Own quality amp; control: Maintain model structure, audit checks, documentation, and version control.
  • Client engagement amp; presentations: Gather requirements and present findings to senior stakeholders.
  • Project management: Deliver on time and on budget while managing risks and dependencies.
  • Mentor amp; elevate the team: Provide coaching, review work products, and support junior staff.
  • Support business development: Shape proposals and architect modeling solutions.
  • Stay current on market, regulatory, and industry trends.
  • Designing and building new models, or significantly changing/updating existing models
Required Qualifications
  • Bachelor’s degree in Finance, Accounting, Economics, Mathematics, Statistics, or related field.
  • 4+ years in consulting, corporate finance, FP amp;A, investment banking, or analytics.
  • Advanced financial modeling: data science, programming, statistics, econometrics.
  • Advanced Excel (XLOOKUP, INDEX/MATCH, SUMIFS, dynamic arrays, data tables).
  • Experience with Power Query/Power Pivot, Python, R, with VBA as a plus.
  • Strong understanding of GAAP financial statements.
  • Consulting skills: structured problem-solving, communication, stakeholder management.
  • Project leadership in fast-paced consulting environments.
Preferred Qualifications
  • SQL familiarity; C+, Java and cloud AWS Azure for advanced analysis (optional).
  • Experience in banking, finance, housing, real estate or capital markets.
  • Certifications: CFA (any level), CPA, FRM
We need client facing talent who currently demonstrate a strong blend of technical skills, and essential soft skills with a commitment to continuous learning in the rapidly evolving field of AI.
  • Previous and current use of productivity tools is required
    • Purpose: answering questions, generating text, help with creating documents, emails, etc.
    • Examples: ChatGPT, MS Copilot, Gemini, etc.
  • Previous and current use of creative tools is desired
    • Purpose: software code, video, graphics generation, etc.
    • Examples: Amazon Q, GitHub Copilot, ect.
  • Experience incorporating AI components directly into solutions is nice to have​
    • Purpose: making AI algorithms part of technology solutions
    • Examples: neural networks, predictive algorithms, large language models, etc.
These individuals should be able to clearly discuss how and when they have used the tools. They understand AI by actively applying it to solve real-world problems and drive business success. Their personality traits:
  • Intellectual Curiosity: A strong desire to learn and explore new AI concepts.
  • Problem-solving amp; Critical Thinking: Ability to apply AI knowledge to new challenges.
  • Learning Agility: Ability to quickly adapt and learn new AI tools and techniques.
Additional requirements:
  • Authorized to work in the United States as a US Citizen. We are not able to accept permanent resident or sponsor or accept any VISA Holders at this time including OPT, H1B, EAD.
  • Successfully pass a background investigation and drug screening.
  • Reside in the DC Metro area within 30 days of hire.
FLSA Designation
  • This is an exempt position.
  • FI Consulting participates in E-Verify.
  • Equal Employment Opportunity/disability/protected veteran status.
  • FI Consulting is committed to working with and providing reasonable accommodation to individuals with physical and mental disabilities. If you need special assistance or an accommodation while seeking employment, please email recruiting@ficonsulting.com or call: 571-255-6772. We will make a determination on your request for reasonable accommodation on a case-by-case basis.
AI plays only a supporting role in our recruitment process. All decisions are made by human members of our talent acquisition team.

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