1

Data Science Machine Learning Jobs in Virginia (NOW HIRING)

Requirements: * BS in data science, machine learning, computer science, statistics, or related highly-quantitative discipline with eight (8) years of experience or equivalent combination of training ...

Associate Data Scientist

Arlington, VA

$67K - $68K/yr

Requirements: * BS in data science, machine learning, computer science, statistics, or related highly-quantitative discipline with three (3) years of experience or equivalent combination of training ...

Requirements: * BS in data science, machine learning, computer science, statistics, or related highly-quantitative discipline with ten (10) years of experience or equivalent combination of training ...

Requirements: * BS in data science, machine learning, computer science, statistics, or related highly-quantitative discipline with eight (8) years of experience or equivalent combination of training ...

You will apply deep expertise in data science, machine learning, AI techniques, large-scale data processing, computational programming, and practical problem solving, with the ability to clearly ...

Bachelor's degree in Data Science, Computer Science, Mathematics, Statistics, or a related field (or equivalent experience) Strong experience in data science, machine learning, and statistical ...

Bachelor's degree in Data Science, Computer Science, Mathematics, Statistics, or a related field (or equivalent experience) Strong experience in data science, machine learning, and statistical ...

next page

Showing results 1-20

Data Science Machine Learning information

See Virginia salary details

$37.2K

$121.7K

$194.8K

How much do data science machine learning jobs pay per year?

As of Jul 22, 2026, the average yearly pay for data science machine learning in Virginia is $121,686.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,700.00 and $134,800.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Data Science Machine Learning professional, and why are they important?

To thrive as a Data Science Machine Learning professional, you need a strong background in statistics, programming (usually Python or R), and a solid understanding of machine learning algorithms, often supported by a degree in computer science, mathematics, or a related field. Familiarity with tools like TensorFlow, scikit-learn, SQL databases, and cloud platforms, as well as certifications such as AWS Certified Machine Learning, are typically valuable. Critical thinking, problem-solving, and effective communication are vital soft skills for interpreting data and collaborating with stakeholders. These skills enable professionals to develop robust models, extract actionable insights, and drive data-driven decision-making in organizations.

What are some common challenges faced when deploying machine learning models as a Data Science Machine Learning professional?

A frequent challenge in this role is bridging the gap between building accurate models in a controlled environment and deploying them effectively in production systems. Issues such as data drift, model performance degradation, and integration with existing IT infrastructure often arise. Collaboration with engineering and IT teams is crucial to ensure models are scalable, maintainable, and secure. Regular monitoring and updating of deployed models are also essential responsibilities to sustain their value to the business.

What is the difference between Data Science Machine Learning vs Data Analyst?

AspectData Science Machine LearningData Analyst
Required SkillsProgramming (Python, R), statistics, machine learning algorithmsData visualization, SQL, basic statistics
Work EnvironmentDeveloping models, coding, experimenting with algorithmsData reporting, dashboard creation, data cleaning
Industry UsageTech, finance, healthcare, where predictive models are neededBusiness intelligence, marketing, operations

Data Science Machine Learning professionals focus on building predictive models and algorithms using programming and advanced statistics, often working on complex projects. Data Analysts primarily interpret data through visualization and reporting to support business decisions. While both roles require data skills, Data Science Machine Learning involves more technical programming and modeling, whereas Data Analysts focus on data interpretation and presentation.

What is data science machine learning?

Data science machine learning refers to the use of algorithms and statistical models to analyze and draw insights from complex data sets. In this field, professionals use machine learning techniques to build predictive models, automate decision-making processes, and uncover patterns in data. Machine learning is a core component of data science, enabling systems to improve their performance over time without being explicitly programmed. Data scientists with machine learning expertise are in high demand across industries like healthcare, finance, and technology.
What cities in Virginia are hiring for Data Science Machine Learning jobs? Cities in Virginia with the most Data Science Machine Learning job openings:
Infographic showing various Data Science Machine Learning job openings in Virginia as of July 2026, with employment types broken down into 1% As Needed, 83% Full Time, 12% Part Time, 1% Temporary, and 3% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $121,686 per year, or $58.5 per hour.
Data Scientist

Full-time

Posted 14 days ago


Job description


What We Do:

Data Scientists at the SEI use advanced statistics, data analytics, machine learning, and artificial intelligence to help our government and industry clients research and solve cybersecurity challenges. In this role, you will work with our customers to identify areas where advanced statistical techniques can help tackle problems, plan and develop prototype solutions, and build out final products. You'll get a chance to work with elite cybersecurity professionals and university faculty to build new technologies that will influence national cybersecurity strategy for decades to come. You will co-author research proposals, execute studies, and present findings to DoW sponsors and at academic conferences.
Our team works on a wide range of projects. Our current work includes research in generative AI and large language models, computer vision, multimodal AI, agentic AI, and assurance of AI systems. Additionally, we craft metrics and experimental designs for large-scale cybersecurity research programs, develop human-in-the-loop machine learning solutions, and build classifiers to identify security vulnerabilities. If you are a data science or statistics expert with an interest in cybersecurity, we want to hear from you!
Requirements:

  • BS in data science, machine learning, computer science, statistics, or related highly-quantitative discipline with eight (8) years of experience or equivalent combination of training or experience; or MS in data science, machine learning, computer science, statistics, or related highly-quantitative discipline with five (5) years of experience; or PhD in data science, machine learning, computer science, statistics, or related highly-quantitative discipline with two (2) years of experience.
  • Willingness to complete modest travel to various locations to support the SEI's overall mission.
  • You will be subject to a background check and must be able obtain and maintain a U.S. Department of War security clearance.

Knowledge, Skills and Abilities:

  • Experience in predictive modeling, data science, and/or AI & machine learning
  • Deep understanding of statistical modeling techniques and advanced data analytics
  • Proficient with at least one mathematical/statistical programming package (e.g., R, python numpy/scipy/pandas/polars, MATLAB, etc.)
  • Innovative and inquisitive with ability to imagine novel analytical solutions to problems Thrives in a multi-disciplinary environment
  • Strong communication skills
  • Expertise in one or more of the following:
  • Recommendation systems
  • Time-series forecasting (Prophet, NeuralProphet, Chronos, Lag-Llama, etc.)
  • NLP / LLMs (fine-tuning, RAG, evaluation, prompt engineering)
  • Causal inference / uplift modeling / synthetic controls
  • Modern ML frameworks: LightGBM/XGBoost, CatBoost, PyTorch,JAX, TensorFlow)
  • LLMs / agentic workflows (LangChain/LlamaIndex/Haystack)
  • Experience deploying models (FastAPI, Triton, KServe, SageMaker, Vertex AI, or similar)
  • Experience working with big data (Spark, Trino, Snowflake, BigQuery, Databricks)

Desired Experience:

  • Experience in cybersecurity and privacy is a plus is a plus
  • Experience in U.S. Government work and/or with FFRDCs, UARCs an National Labs is a plus
  • Demonstrated ability to learn new concepts and grow into new areas of work

Location

Arlington, VA, Pittsburgh, PA

Job Function

Software/Applications Development/Engineering

Position Type

Staff - Regular

Full time/Part time

Full time

Pay Basis

SalaryMore Information:
  • Please visit "Why Carnegie Mellon" to learn more about becoming part of an institution inspiring innovations that change the world.

  • Click here to view a listing of employee benefits

  • Carnegie Mellon University is an Equal Opportunity Employer/Disability/Veteran.

  • Statement of Assurance


CMU logo

About CMU

Sourced by ZipRecruiter

Industry

Offices of mental health practitioners

Company size

201 - 500 Employees

Headquarters location

Harrisburg, PA, US