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Scientific Machine Learning Jobs in West Virginia

Data Science and Data Engineering Job Qualifications: Skills: Analytics, End-to-End Testing ... Deliver AI and machine learning solutions across a variety of domains, including natural language ...

Generative AI Strategist

Charleston, WV · On-site

$112K - $145K/yr

... of generative AI and machine learning? Join us in driving strategic alignment, fostering ... Work closely with various teams within the customer's organization, including applied scientists ...

WV · On-site

Data Science and Data Engineering Job Qualifications: Skills: Amazon Bedrock, Amazon Web Services (AWS), Artificial Intelligence (AI), Machine Learning (ML), Retrieval-Augmented Generation ...

Role Summary A senior data scientist is responsible for leading the development and integration of advanced artificial intelligence and machine learning models within enterprise data platforms. This ...

... machine learning in assessing and monitoring risk. This role is highly cross-functional, partnering closely with underwriters, data science, and other engineering teams to scale and automate ...

... Science or Biometrics. Background in biometrics, image/video/signal processing, pattern recognition, computer vision, machine learning and statistical analysis. Experience in C/C++, C#, Java/J2EE ...

Showing results 41-60

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 West Virginia?

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

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

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

Infographic showing various Scientific Machine Learning job openings in West Virginia as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 24% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

AI/ML Delivery Engineer

GDIT

WV • On-site, Remote

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 3 days ago


General Dynamics Information Technology rating

7.8

Company rating: 7.8 out of 10

Based on 63 frontline employees who took The Breakroom Quiz

87th of 224 rated it services


Job description

Type of Requisition:

Regular

Clearance Level Must Currently Possess:

None

Clearance Level Must Be Able to Obtain:

None

Public Trust/Other Required:

NACLC (T3)

Job Family:

Data Science and Data Engineering

Job Qualifications:

Skills:

Analytics, End-to-End Testing, Solution Architecture

Certifications:

None

Experience:

10 + years of related experience

US Citizenship Required:

No

Job Description:

The AI/ML Delivery Engineer is a hands-on senior practitioner who designs, builds, and scales enterprise AI, ML, and data products across cloud environments. This role combines deep engineering expertise with solution architecture, delivery leadership, and executive-level communication. This role supports the NIH mission by delivering secure, scalable, and innovative AI and data solutions that accelerate biomedical research, improve operational efficiency, and enable data-driven decision-making to advance human health.
MEANINGFUL WORK AND PERSONAL IMPACT

The ideal candidate can move from strategy to production implementation across intelligent search, generative AI chat, agentic workflows, predictive ML, computer vision, and medical AI imaging. They bridge data science, software engineering, platform engineering, and security to deliver robust, governed, cost-aware, fit-for-mission AI solutions.

Solution Design and Delivery

Architect and deliver end-to-end AI/ML and generative AI solutions across the full lifecycle, including data integration, feature engineering, model development, evaluation, deployment, monitoring, and continuous improvement.

Design scalable, secure, and governed AI/ML architectures that support enterprise data management, cloud-native services, distributed computing, and high-performance workloads.

Develop and deploy production-ready AI solutions using modern techniques for large language models, intelligent search, workflow automation, model evaluation, monitoring, and continuous optimization.

Integrate enterprise AI and cloud services with organizational data platforms, security controls, governance frameworks, and operational processes.

Deliver AI and machine learning solutions across a variety of domains, including natural language processing, computer vision, predictive analytics, intelligent search, multimodal AI, and other mission-focused applications, using industry-standard frameworks and tools.

Design secure application programming interfaces (APIs), integration services, data pipelines, and orchestration workflows that enable AI capabilities to be reliably consumed by enterprise systems and end users.

Technical Leadership, Governance, and Delivery Excellence

Serve as the AI/ML technical authority for cross-functional data science, engineering, platform, security, infrastructure, and business teams.

Lead architecture reviews, AI readiness assessments, performance benchmarking, and infrastructure trade-off analysis for large-scale cloud, GPU, and distributed data workloads.

Establish MLOps, LLMOps, and ModelOps practices for CI/CD, experiment tracking, model registry, prompt/model versioning, automated testing, deployment promotion, rollback, drift/quality monitoring, lineage, and cost optimization.

Define responsible AI controls for privacy, IAM, key management, audit logging, PHI/PII handling, explain ability, model cards, bias/risk assessment, human oversight, and agentic guardrails.

Mentor engineers and data scientists while communicating complex AI concepts through clear solution narratives, architecture diagrams, demonstrations, and executive-ready materials.

Support solutioning activities by shaping technical approaches, estimating delivery patterns, and contributing to client responses.

WHAT YOU'LL NEED TO SUCCEED


Bring your expertise and drive for innovation to GDIT. The AI Engineer Sr Principal must have:


Education: Master of Science in Computer Science, Information Technology, Engineering, Mathematics/Statistics, Bioinformatics, Data Science, or equivalent professional experience.

Experience: 10+ years of related experience

Hands-on experience with modern enterprise data platforms and cloud-native data architectures, including distributed data processing, data governance, machine learning lifecycle management, and batch and streaming data pipelines.

Production experience developing, deploying, and supporting AI/ML and generative AI solutions, including intelligent search, large language model (LLM) applications, workflow automation, model serving, evaluation, monitoring, and continuous improvement.

Experience with modern machine learning and deep learning frameworks, libraries, and tools for developing, training, evaluating, and deploying AI/ML solutions.

Experience designing, deploying, and managing AI/ML solutions in one or more major cloud environments, including cloud-native AI services, identity and access management, networking, security, and infrastructure.

Experience working with large, complex, sensitive, or regulated datasets, including data integration, migration, quality management, analytics, visualization, and governance in government, healthcare, or other regulated environments.

Excellent written and verbal communication skills, with the ability to collaborate effectively with technical teams, business stakeholders, and executive leadership.

Security clearance level: Must be able to obtain a Tier 3 Public Trust
Must be a US Person

This position is fully remote; If selected, travel to NIH at applicant's expense will be required for onboarding.


GDIT IS YOUR PLACE


At GDIT, the mission is our purpose, and our people are at the center of everything we do.
Growth: AI-powered career tool that identifies career steps and learning opportunities
Support: An internal mobility team focused on helping you achieve your career goals
Rewards: Comprehensive benefits and wellness packages, 401K with company match, and competitive pay and paid time off
Flexibility: Full-flex work week to own your priorities at work and at home
Community: Award-winning culture of innovation and a military-friendly workplace
OWN YOUR OPPORTUNITY
Explore a career in data science and engineering at GDIT and you'll find endless opportunities to grow alongside colleagues who share your determination for solving complex data challenges.

#GDITHealth

#GDITFedHealthJobs

The likely salary range for this position is $164,382 - $207,000. This is not, however, a guarantee of compensation or salary. Rather, salary will be set based on experience, geographic location and possibly contractual requirements and could fall outside of this range.

Scheduled Weekly Hours:

40

Travel Required:

None

Telecommuting Options:

Remote

Work Location:

Any Location / Remote

Additional Work Locations:

Total Rewards at GDIT:

Our benefits package for all US-based employees includes a variety of medical plan options, some with Health Savings Accounts, dental plan options, a vision plan, and a 401(k) plan offering the ability to contribute both pre and post-tax dollars up to the IRS annual limits and receive a company match. To encourage work/life balance, GDIT offers employees full flex work weeks where possible and a variety of paid time off plans, including vacation, sick and personal time, holidays, paid parental, military, bereavement and jury duty leave. GDIT typically provides new employees with 15 days of paid leave per calendar year to be used for vacations, personal business, and illness and an additional 10 paid holidays per year. Paid leave and paid holidays are prorated based on the employee's date of hire. The GDIT Paid Family Leave program provides a total of up to 160 hours of paid leave in a rolling 12 month period for eligible employees. To ensure our employees are able to protect their income, other offerings such as short and long-term disability benefits, life, accidental death and dismemberment, personal accident, critical illness and business travel and accident insurance are provided or available. We regularly review our Total Rewards package to ensure our offerings are competitive and reflect what our employees have told us they value most.

Our Identity Verification Process:

As part of the hiring process, we will ask you to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud. You are expected to be on camera during virtual interviews. We reserve the right to take your picture to verify your identity and prevent fraud. By proceeding, you authorize the collection, processing, and use of your biometric data for identity verification and security purposes.

About Our Work:

We are GDIT. A global technology and professional services company that delivers technology solutions and mission services to every major agency across the U.S. government, defense and intelligence community. Our 26,000 experts extract the power of technology to create immediate value and deliver solutions at the edge of innovation. We operate across 50+ countries worldwide, offering leading mission-ready capabilities in AI, cloud, cyber and software development.Join our Talent Community to stay up to date on our career opportunities and events at

gdit.com/tc.

Equal Opportunity Employer / Individuals with Disabilities / Protected Veterans

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About General Dynamics Information Technology

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GDIT is a global technology and professional services company that delivers technology solutions and mission services to every major agency across the U.S. government, defense, and intelligence community. Its 30,000 experts extract the power of technology to create immediate value and deliver solutions at the edge of innovation. The company operates across 50+ countries worldwide, offering leading capabilities in digital modernization, AI/ML, cloud, cyber, and application development.

Industry

It services

Company size

10,000+ Employees

Headquarters location

Falls Church, VA, US