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Scientific Machine Learning Jobs in Virginia (NOW HIRING)

Hirekeyz Inc

Sr. Machine Learning Engineer

Fort Belvoir, VA · 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 ...

Cymertek

Machine Learning Engineer

Chantilly, VA · On-site

Machine Learning Engineer LOCATION Chantilly, VA 20151 CLEARANCE TS/SCI Full Poly (Please note this ... You will collaborate with data scientists, engineers, and product teams to turn data into ...

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Scientific Machine Learning information

What is scientific machine learning?

Scientific machine learning (SciML) is an interdisciplinary field that combines principles from machine learning and scientific computing to solve complex scientific and engineering problems. It involves developing algorithms and models that can learn from data and physical laws, such as differential equations, to make predictions, optimize systems, or gain insights into phenomena. SciML is widely used in areas like physics, biology, climate science, and engineering, enabling researchers to accelerate simulations and make data-driven discoveries. The field often leverages both traditional numerical methods and modern machine learning techniques, making it a rapidly evolving area of research.

What are the key skills and qualifications needed to thrive as a scientific machine learning professional, and why are they important?

To thrive as a Scientific Machine Learning professional, you need a strong background in mathematics, statistics, programming (often Python), and domain-specific scientific knowledge, typically with a graduate degree in a STEM field. Proficiency in machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools (like NumPy, SciPy), and experience with high-performance computing are commonly required. Critical thinking, problem-solving, and collaborative communication are vital soft skills for designing experiments and interpreting complex data. These skills ensure robust, reproducible results and the ability to bridge scientific inquiry with advanced computational methods.

What are some common challenges faced by professionals in scientific machine learning, and how can they be addressed?

Professionals in Scientific Machine Learning often encounter challenges such as integrating domain-specific scientific knowledge with machine learning models, managing large and complex datasets, and ensuring that models are interpretable and physically consistent. Collaboration with domain experts and interdisciplinary teams is essential to bridge knowledge gaps and validate results. To address these challenges, it is helpful to invest time in understanding the underlying scientific principles, keep up-to-date with advancements in both machine learning and scientific fields, and utilize specialized tools and frameworks designed for scientific data.

What is the difference between Scientific Machine Learning vs Data Scientist?

AspectScientific Machine LearningData Scientist
Required credentialsAdvanced degrees in CS, ML, or related fields; knowledge of scientific computingDegree in CS, statistics, or related fields; strong analytical skills
Work environmentResearch labs, academia, industry R&D teamsBusiness analytics, tech companies, consulting firms
Industry usageResearch, scientific computing, engineering simulationsBusiness insights, predictive modeling, data analysis

Scientific Machine Learning focuses on integrating scientific knowledge with machine learning techniques for research and engineering applications. Data Scientists analyze data to extract insights and build predictive models for business or operational purposes. While both roles require strong technical skills, Scientific Machine Learning emphasizes scientific computing and domain-specific modeling, whereas Data Scientists focus on data analysis and visualization.

What are popular job titles related to Scientific Machine Learning jobs in Virginia?

For Scientific Machine Learning jobs in Virginia, the most frequently searched job titles are:

What job categories do people searching Scientific Machine Learning jobs in Virginia look for?

The top searched job categories for Scientific Machine Learning jobs in Virginia are:

What cities in Virginia are hiring for Scientific Machine Learning jobs?

Cities in Virginia with the most Scientific Machine Learning job openings:

Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in Virginia as of August 2026, with employment types broken down into 1% As Needed, 70% Full Time, 28% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Data Scientist / Machine Learning Engineer

Arlington, VA • On-site

Electronic Consulting Services, Inc (ECS Federal)
See more jobs at Electronic Consulting Services, Inc (ECS Federal)external

$160K - $185K/yr

Other

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

Job description

Job Description
Everforth ECS is seeking a Data Scientist/Machine Learning Engineer to join our team in Arlington, VA (Hybrid) . This position is contingent upon award.
We are seeking a talented Data Scientist / Machine Learning Engineer to design, develop, deploy, and optimize advanced analytics and machine learning solutions that drive business insights and operational efficiencies. This role combines data science, machine learning engineering, and software development to transform complex data into scalable, production-ready AI and predictive analytics solutions.
The ideal candidate will possess expertise in statistical analysis, machine learning algorithms, AI/ML-assisted clustering, feature engineering, model deployment, anomaly detection, and cloud-based AI platforms while collaborating closely with business stakeholders, data engineers, and technology teams.
Key Responsibilities
Data Science & Advanced Analytics
  • Analyze structured and unstructured data to identify trends, patterns, and actionable insights.
  • Develop predictive, prescriptive, and classification models to support business objectives.
  • Perform exploratory data analysis (EDA), feature engineering, and statistical modeling.
  • Design experiments and evaluate model performance using appropriate statistical methodologies.
  • Present findings and recommendations to technical and non-technical stakeholders.
  • Support efforts in anomaly detection.
Machine Learning Development
  • Design, build, train, and optimize machine learning and deep learning models.
  • Develop solutions for forecasting, anomaly detection, natural language processing (NLP), recommendation systems, and computer vision applications.
  • Evaluate and select appropriate algorithms based on business requirements and performance objectives.
  • Continuously improve model accuracy, scalability, and maintainability.
MLOps & Production Engineering
  • Deploy machine learning models into production environments.
  • Build automated model training, validation, deployment, and monitoring pipelines.
  • Implement CI/CD practices for machine learning workflows.
  • Support AI/ML-assisted clustering efforts.
  • Monitor model performance and address model drift, data drift, and operational issues.
  • Maintain model governance, versioning, and documentation standards.
Data Engineering & Platform Integration
  • Collaborate with data engineers to develop scalable data pipelines and feature stores.
  • Integrate machine learning solutions into enterprise applications and business processes.
  • Optimize data processing workflows for large-scale datasets.
  • Ensure data quality, security, and compliance standards are maintained.
Cloud & AI Platforms
  • Develop and deploy solutions using cloud-native AI and machine learning services.
  • Leverage platforms such as Azure Machine Learning, AWS SageMaker, Databricks, Vertex AI, or equivalent technologies.
  • Perform multi-source summarization with human-review workflow by combining AI-driven aggregation of diverse sources with targeted human validation.
  • Utilize distributed computing frameworks to support large-scale analytics workloads.
  • Support enterprise AI strategy and modernization initiatives.
Collaboration & Innovation
  • Partner with business leaders to identify opportunities for AI and advanced analytics solutions.
  • Translate business requirements into machine learning use cases and technical requirements.
  • Stay current on emerging technologies, AI trends, and industry best practices.
  • Contribute to innovation initiatives, proofs of concept, and research activities.
Salary Range: $160,000 - $185,000
General Description of Benefits
Required Skills
  • Top Secret Clearance
  • Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
  • 5+ years of experience in Data Science, Machine Learning Engineering, Artificial Intelligence, or Advanced Analytics.
  • Strong understanding of machine learning algorithms, statistical analysis, and data modeling techniques.
  • Experience building and deploying machine learning models in production environments.
  • Proficiency in Python and machine learning libraries/frameworks.
  • Strong knowledge of SQL and data manipulation techniques.
  • Experience working with large datasets and cloud-based data platforms.
  • Excellent problem-solving, analytical, and communication skills.

Desired Skills
  • Master's degree in Data Science, Machine Learning, Artificial Intelligence, Statistics, or related discipline.
  • Experience with Generative AI and Large Language Models (LLMs).
  • Familiarity with Retrieval-Augmented Generation (RAG) architectures.
  • Experience supporting enterprise AI transformation initiatives.
  • Knowledge of model governance, responsible AI, and AI risk management frameworks.
  • Experience working in highly regulated industries such as healthcare, finance, government, or defense.
#EverforthECS1
ECS Federal LLC is an equal opportunity employer and does not discriminate or allow discrimination on the basis any characteristic protected by law. All qualified applicants will receive consideration for employment without regard to disability, status as a protected veteran or any other status protected by applicable federal, state, or local jurisdiction law.
Everforth ECS is the federal segment of Everforth , a $4B global organization with over 10,000 employees. Our nearly 3,500 professionals deliver advanced technology solutions in data and AI, cybersecurity, and enterprise transformation, serving defense, intelligence, and federal civilian agencies.
Our work powers mission-critical outcomes, strengthens technology partnerships, and creates meaningful opportunities for our people. We are defined by a commitment to excellence in delivery, a culture of innovation, and an environment where talent can thrive and grow.
We value:
  • Attracting and developing top talent and high-performing teams
  • Fostering a culture that is engaging, accountable, and mission-driven

Meet the challenge. Make a difference with Everforth ECS!

Apply