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Data Science Machine Learning Jobs in Virginia (NOW HIRING)

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 ...

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 ...

Required : โ€ข Master's in computer science, Machine Learning, or higher level degree is preferred with of 3+ years of related industry experience in Machine Learning, Computer Science, Data Science ...

Required : โ€ข Master's in computer science, Machine Learning, or higher level degree is preferred with of 3+ years of related industry experience in Machine Learning, Computer Science, Data Science ...

Sr. Machine Learning Engineer

Fort Belvoir, VA ยท On-site

$118K - $162K/yr

Master's Degree in Data Science, Machine Learning, or a related field * Proven experience in a machine learning or AI engineering role * Strong proficiency in Python, C++, or Java * Extensive ...

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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 Jun 9, 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 May 2026, with employment types broken down into 1% As Needed, 91% Full Time, and 8% Part Time. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $121,686 per year, or $58.5 per hour.

Data Scientist

KDA Consulting Inc

Mclean, VA โ€ข On-site

Full-time

Posted 4 days ago


Job description

KDA Consulting Inc. is seeking a highly skilled Data Scientist with AI/ML expertise to support mission-critical programs within the Intelligence Community (IC). This role will focus on leveraging advanced analytics, machine learning, and artificial intelligence to extract insights from large, complex datasets and support data-driven decision-making.

The ideal candidate will have a strong foundation in statistical analysis, machine learning model development, and data visualization, along with the ability to translate complex findings into actionable insights for both technical and non-technical stakeholders.

Machine Learning & AI Development

  • Design, develop, and deploy machine learning models to solve complex mission problems
  • Build predictive and prescriptive analytics solutions to support operational and strategic decision-making
  • Evaluate model performance and continuously improve algorithms through testing and tuning

Data Analysis & Exploration

  • Analyze large, structured and unstructured datasets to identify trends, patterns, and anomalies
  • Perform data cleansing, feature engineering, and transformation to prepare data for modeling
  • Apply statistical techniques to validate hypotheses and support analytical findings

Data Visualization & Communication

  • Develop dashboards, visualizations, and reports using tools such as Tableau, Power BI, or Python visualization libraries
  • Communicate insights and recommendations clearly to both technical teams and senior leadership
  • Translate complex analytical results into actionable business or mission outcomes

Model Deployment & Integration

  • Collaborate with data engineers and software developers to operationalize models into production environments
  • Integrate machine learning solutions into enterprise systems and workflows
  • Support cloud-based model deployment in environments such as AWS or Azure

Collaboration & Agile Delivery

  • Work closely with cross-functional teams including engineers, analysts, and mission stakeholders
  • Participate in Agile processes including sprint planning, stand-ups, and retrospectives
  • Contribute to continuous improvement of data science methodologies and processes

Requirements

Active TS/SCI W/ Polygraph Required.

Bachelorโ€™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 analysis

Proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn

Experience analyzing and working with large-scale datasets

Strong understanding of statistical modeling, probability, and data analysis techniques

Experience with data visualization tools and communicating insights effectively

Strong problem-solving skills and ability to work in complex, mission-driven environments