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AI/ML Data Scientist

System One

McLean, Virginia • 2d ago

Easy Apply

Contract

Depends on Experience

AI/ML Data Scientist

Navigant Consulting

Arlington, Virginia • Today

Full-time

Associate AI/ML Data Scientist (Systems Architecture, Associate)

MIL Corporation

Reston, Virginia • Today

Full-time

USD 150,000.00 - 180,000.00 per year

AI/ML Data Scientist - AI Vector Search & Agentic AI Platforms

Red Arch Solutions

Reston, Virginia • Today

Full-time

USD 180,000.00 - 220,000.00 per year

AI/ML Data Scientist - AI, Vector Search & Agentic AI Platforms

Red Arch Solutions

Reston, Virginia • Today

Full-time

USD 220,000.00 - 250,000.00 per year

Data Scientist - AI/ML Specialist

CACI International, Inc.

Fairfax, Virginia • Today

Full-time

USD 94,400.00 - 198,200.00 per year

Cybersecurity AI/ML Lead - Data Scientist

JPMorgan Chase & Co.

McLean, Virginia • Today

Full-time

USD 171,000.00 - 260,000.00 per year

AI/ML Engineer and Data Scientist

Booz Allen Hamilton

Riverdale Park, Maryland • Today

Full-time

USD 77,600.00 - 176,000.00 per year

Senior Data Scientist / AI Machine Learning Research Engineer

CACI International, Inc.

Sterling, Virginia • Today

Full-time

USD 113,200.00 - 237,800.00 per year

Senior Principal Data Scientist / AI-ML SME (Analytic Superiority)

Leidos

Fort Meade, Maryland • Today

Full-time

USD 154,050.00 - 278,475.00 per year

AI Data Scientist

Fiserv, Inc.

Washington, District of Columbia • Yesterday

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USD 90,000.00 - 152,400.00 per year

Fraud AI/ML Data Scientist

Navigant Consulting

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Full-time

USD 80,000.00 - 133,000.00 per year

AI Data Scientist (REMOTE)

Koniag, Inc.

Remote or Washington, District of Columbia • Today

Full-time

USD 135,000.00 per year

AI Data Scientist

Leidos

Chantilly, Virginia • Today

Full-time

USD 131,300.00 - 237,350.00 per year

AI Data Scientist

Unissant, Inc.

Ashburn, Virginia • 6d ago

Easy Apply

Full-time

Depends on Experience

Data Scientist, Sr - Artificial Intelligence, Cloud & Automation (Open)

Leidos

Fort Meade, Maryland • Today

Full-time

USD 131,300.00 - 237,350.00 per year

Data Scientist / Machine Learning (TS SCI + Poly is Required)

Aperio Global

Fort Meade, Maryland • Today

Full-time

Data Scientist with Neural Networks AND Machine Learning

ApTask

Remote • 23d ago

Easy Apply

Contract

50 - 55

Mid Data Scientist

Booz Allen Hamilton

Reston, Virginia • Today

Full-time

USD 77,600.00 - 176,000.00 per year

Data Scientist

Satsyil Corporation

Hybrid in Arlington, Virginia • 2d ago

Easy Apply

Full-time

Depends on Experience

Data Scientist

Software Guidance & Assistance

Rockville, Maryland • Today

Contract

USD 80.00 - 85.00 per hour

Senior Data Scientist

Booz Allen Hamilton

McLean, Virginia • Today

Full-time

USD 99,000.00 - 225,000.00 per year

Data Scientist

American Bureau of Shipping

Washington, District of Columbia • Today

Full-time

University - Data Scientist

Booz Allen Hamilton

Hybrid in McLean, Virginia • Today

Full-time

USD 62,000.00 - 141,000.00 per year

Summer Associate Internship (Data Scientist)

Navy Federal Credit Union

Hybrid in Vienna, Virginia • Today

Full-time

Junior Data Scientist

Booz Allen Hamilton

Washington, District of Columbia • Today

Full-time

USD 62,000.00 - 141,000.00 per year

2027 Summer Intern, Data Scientist (Reston, VA; Denver, CO; Remote)

ICF Consulting Group, Inc.

Remote or Reston, Virginia • Today

Part-time

USD 45,805.00 - 94,221.00 per year

Junior Level Data Scientist

Absolute Business Solutions Corp

Falls Church, Virginia • 6d ago

Full-time

Mid Data Scientist

Booz Allen Hamilton

Washington, District of Columbia • Today

Full-time

USD 77,600.00 - 176,000.00 per year

Lead Data Scientist / Data Architect

Essnova Solutions

Suitland-Silver Hill, Maryland • Today

Full-time

System One

Job Details

Skills

Summary

Job Title: AI/ML Data Scientist
Location: 100% REMOTE
Clearance: Ability to obtain and maintain a Public Trust clearance.
Type: Contract-to-hire
Contact:

WHAT YOU WILL DO:

Partner with stakeholders to define and deliver AI, machine learning, and advanced analytics use cases, translating business needs into scalable data science solutions.
Design and develop machine learning models and analytical approaches to support search, discovery, anomaly detection, fraud detection, and insight generation across structured and unstructured data.
Develop anomaly and fraud detection analyses using supervised, unsupervised, semi-supervised, statistical, and graph-based techniques, including outlier detection, behavioral profiling, risk scoring, pattern detection, and relationship analysis.
Use Generative AI and foundation models to enhance anomaly and fraud detection workflows through alert enrichment, case summarization, contextual analysis, evidence synthesis, pattern explanation, investigative hypothesis generation, and analyst decision support.
Build and implement natural language processing, semantic search, entity resolution, and relationship analytics capabilities to enable advanced information retrieval and identification of suspicious patterns and connections.
Leverage document-based data, including OCR/ICR outputs, metadata, images, extracted fields, and free text, to support downstream analytics, search, anomaly detection, and fraud analysis.
Collaborate with data engineers, cloud engineers, investigators, and business stakeholders to integrate models and analytical capabilities into production environments using AWS-native services.
Develop and operationalize data science solutions within an AWS data lakehouse, including scalable data preparation, feature engineering, model training, inference, monitoring, and analytics.
Develop model evaluation frameworks, confidence and risk scoring, explainability, traceability, and human-in-the-loop review approaches to ensure AI outputs are transparent, actionable, and suitable for investigative and operational use.
Evaluate anomaly and fraud detection models using appropriate measures, including precision, recall, F1 score, false-positive rate, detection rate, ranking quality, and business impact.
Support the development of dashboards, reporting, alerting, and investigative workflows that drive operational insights and informed decision-making.
Operate within an Agile delivery model, contributing to sprint planning, experimentation, model iteration, and incremental solution delivery.
Communicate findings, risk indicators, model limitations, and recommendations clearly to technical and non-technical audiences, including client stakeholders.
Contribute to solution design, proposal support, technical documentation, and thought leadership in AI, analytics, anomaly detection, and fraud detection.

WHAT YOU WILL NEED:

Bachelors or Masters degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field.
A minimum of 4 years of experience in data science, machine learning, artificial intelligence, or applied analytics roles.
Ability to obtain and maintain a Public Trust clearance. U.S. Citizenship preferred.
Experience developing and applying machine learning models in one or more of the following areas:
Natural Language Processing (NLP)
Semantic search or information retrieval
Entity resolution or relationship modeling
Anomaly or outlier detection
Fraud, risk, waste, abuse, or suspicious-pattern detection
Graph analytics or network analysis

Skills and experience designing or implementing anomaly and fraud detection analyses using methods such as classification, clustering, isolation-based techniques, autoencoders, time-series analysis, behavioral analytics, link analysis, rules-based detection, or ensemble modeling.
Demonstrated competency working with AWS-native data, analytics, AI, and machine learning services, such as Amazon S3, AWS Glue, AWS Lake Formation, Amazon Athena, Amazon EMR, Amazon Redshift, Amazon SageMaker, Amazon Bedrock, AWS Lambda, AWS Step Functions, Amazon OpenSearch Service, Amazon ECS or Amazon EKS, and Amazon CloudWatch.
Demonstrated competency working with AWS data lakehouse architectures, including Amazon S3-based data lakes, Apache Iceberg or similar open table formats, centralized metadata catalogs, governed data access, schema evolution, partitioning, data quality, and scalable query and transformation patterns.
Demonstrated competency using Generative AI, large language models, or foundation models for anomaly and fraud detection analysis, including alert enrichment, investigative summarization, contextual reasoning, evidence synthesis, pattern explanation, and analyst assistance.
Experience working with large-scale structured and unstructured data, particularly document-based datasets such as text, PDFs, images, extracted fields, and metadata.
Experience using metadata, engineered features, embeddings, event or transaction histories, and relationship data to support analytics and modeling.
Strong proficiency in Python for data science and machine learning, including libraries such as Pandas, NumPy, Scikit-learn, PyTorch, or TensorFlow, along with strong SQL skills.
Experience building scalable data processing and machine learning pipelines using AWS-native services and open-source frameworks such as Apache Spark.
Experience integrating models into production environments through APIs, batch pipelines, event-driven workflows, streaming pipelines, or embedded analytics applications.
Understanding of model evaluation, validation, monitoring, drift detection, bias assessment, explainability, and performance measurement.
Understanding of operational challenges in anomaly and fraud detection, including class imbalance, changing patterns, false positives, data leakage, concept drift, and feedback-loop design.
Ability to apply responsible AI, privacy, security, and governance practices when using Generative AI with sensitive or regulated data.
Strong communication skills and the ability to translate analytical outputs, detected risks, and model results into actionable insights.
Experience working in cross-functional, matrixed teams in an Agile environment.

What Would Be Nice To Have:

Experience developing production-grade anomaly detection, fraud detection, waste and abuse analytics, risk-scoring, or investigative decision-support solutions.
Experience using Amazon Bedrock foundation models or comparable enterprise Generative AI capabilities for analytics, anomaly detection, fraud investigation, or case-management workflows.
Experience implementing Retrieval-Augmented Generation (RAG), prompt engineering, agentic workflows, guardrails, foundation-model evaluation, and responsible AI controls for enterprise use cases.
Experience building AI-enabled search solutions, including semantic search, hybrid search, document retrieval, embeddings, and ranking models using AWS-native services.
Experience with multimodal data processing involving text, images, documents, metadata, and other data types.
Familiarity with OCR/ICR and document intelligence pipelines using services such as Amazon Textract and Amazon Bedrock.
Experience using Amazon SageMaker for feature engineering, model development, training, deployment, monitoring, and machine learning lifecycle management.
Experience using Amazon OpenSearch Service, graph databases, or graph-processing frameworks for relationship mapping, link analysis, and suspicious-network detection.
Experience developing explainable AI solutions, including confidence scoring, reason codes, feature attribution, evidence traceability, and analyst-friendly explanations.
Experience designing analytics dashboards, alert-management interfaces, investigative reporting, or case-triage solutions for end users.
Knowledge of AWS security and governance practices, including IAM, encryption, logging, data classification, least-privilege access, and handling sensitive or regulated information.
Previous experience supporting federal clients or working in regulated environments.
Consulting experience or experience in a client-facing delivery role strongly preferred.
Experience supporting training, user enablement, or adoption of analytics capabilities across teams.
Familiarity with graph-based analytics, ontology-driven models, knowledge graphs, or relationship mapping.

System One, and its subsidiaries including Joul and Mountain Ltd., are leaders in delivering outsourced services and workforce solutions across North America. We help clients get work done more efficiently and economically, without compromising quality. System One not only serves as a valued partner for our clients, but we offer eligible employees health and welfare benefits coverage options including medical, dental, vision, spending accounts, life insurance, voluntary plans, as well as participation in a 401(k) plan.

System One is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, age, national origin, disability, family care or medical leave status, genetic information, veteran status, marital status, or any other characteristic protected by applicable federal, state, or local law.

#M1
#LI-CD1
Ref: #851-Rockville-S1

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 10295162
Position Id: 381091
Posted 30+ days ago

Company Info

About System One

System One is a leading provider of specialized, highly technical outsourced services, workforce solutions, staffing, and recruiting to critical infrastructure, technology, life sciences, and government sectors As an essential partner to private and public organizations of various needs and sizes, we offer our expertise to help them bring their most complex and mission-critical programs to fruition.

With our highly specialized services, in-demand technical skills, and vast operational expertise, we can deliver accelerated results that benefit our clients and the job seekers who partner with us. We staff contract, contract-to-hire, and direct-hire professionals across our network to work with our clients, some of whom are industry leaders and nationally branded organizations.

Our dedicated team of recruiters work tirelessly to match jobseekers with careers that align with their skills, aspirations, and goals.

We understand that finding the right job can be a transformative experience, and we are committed to supporting jobseekers throughout their career journeys. Our recruitment services are designed to connect individuals with fulfilling jobs that offer growth and development opportunities.

Whether you're an organization seeking customized workforce solutions, outsourced services, or if you're a jobseeker searching for your next opportunity,

System One is the perfect staffing partner for you.

Fitment

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Contact the job poster

Crystal Dinnocenti

Technical Recruiter | Specializing in Cleared Talent for DoD & Intel Agencies @ System One

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System One

Job Details

Skills

Summary

Job Title: AI/ML Data Scientist
Location: 100% REMOTE
Clearance: Ability to obtain and maintain a Public Trust clearance.
Type: Contract-to-hire
Contact:

WHAT YOU WILL DO:

Partner with stakeholders to define and deliver AI, machine learning, and advanced analytics use cases, translating business needs into scalable data science solutions.
Design and develop machine learning models and analytical approaches to support search, discovery, anomaly detection, fraud detection, and insight generation across structured and unstructured data.
Develop anomaly and fraud detection analyses using supervised, unsupervised, semi-supervised, statistical, and graph-based techniques, including outlier detection, behavioral profiling, risk scoring, pattern detection, and relationship analysis.
Use Generative AI and foundation models to enhance anomaly and fraud detection workflows through alert enrichment, case summarization, contextual analysis, evidence synthesis, pattern explanation, investigative hypothesis generation, and analyst decision support.
Build and implement natural language processing, semantic search, entity resolution, and relationship analytics capabilities to enable advanced information retrieval and identification of suspicious patterns and connections.
Leverage document-based data, including OCR/ICR outputs, metadata, images, extracted fields, and free text, to support downstream analytics, search, anomaly detection, and fraud analysis.
Collaborate with data engineers, cloud engineers, investigators, and business stakeholders to integrate models and analytical capabilities into production environments using AWS-native services.
Develop and operationalize data science solutions within an AWS data lakehouse, including scalable data preparation, feature engineering, model training, inference, monitoring, and analytics.
Develop model evaluation frameworks, confidence and risk scoring, explainability, traceability, and human-in-the-loop review approaches to ensure AI outputs are transparent, actionable, and suitable for investigative and operational use.
Evaluate anomaly and fraud detection models using appropriate measures, including precision, recall, F1 score, false-positive rate, detection rate, ranking quality, and business impact.
Support the development of dashboards, reporting, alerting, and investigative workflows that drive operational insights and informed decision-making.
Operate within an Agile delivery model, contributing to sprint planning, experimentation, model iteration, and incremental solution delivery.
Communicate findings, risk indicators, model limitations, and recommendations clearly to technical and non-technical audiences, including client stakeholders.
Contribute to solution design, proposal support, technical documentation, and thought leadership in AI, analytics, anomaly detection, and fraud detection.

WHAT YOU WILL NEED:

Bachelors or Masters degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field.
A minimum of 4 years of experience in data science, machine learning, artificial intelligence, or applied analytics roles.
Ability to obtain and maintain a Public Trust clearance. U.S. Citizenship preferred.
Experience developing and applying machine learning models in one or more of the following areas:
Natural Language Processing (NLP)
Semantic search or information retrieval
Entity resolution or relationship modeling
Anomaly or outlier detection
Fraud, risk, waste, abuse, or suspicious-pattern detection
Graph analytics or network analysis

Skills and experience designing or implementing anomaly and fraud detection analyses using methods such as classification, clustering, isolation-based techniques, autoencoders, time-series analysis, behavioral analytics, link analysis, rules-based detection, or ensemble modeling.
Demonstrated competency working with AWS-native data, analytics, AI, and machine learning services, such as Amazon S3, AWS Glue, AWS Lake Formation, Amazon Athena, Amazon EMR, Amazon Redshift, Amazon SageMaker, Amazon Bedrock, AWS Lambda, AWS Step Functions, Amazon OpenSearch Service, Amazon ECS or Amazon EKS, and Amazon CloudWatch.
Demonstrated competency working with AWS data lakehouse architectures, including Amazon S3-based data lakes, Apache Iceberg or similar open table formats, centralized metadata catalogs, governed data access, schema evolution, partitioning, data quality, and scalable query and transformation patterns.
Demonstrated competency using Generative AI, large language models, or foundation models for anomaly and fraud detection analysis, including alert enrichment, investigative summarization, contextual reasoning, evidence synthesis, pattern explanation, and analyst assistance.
Experience working with large-scale structured and unstructured data, particularly document-based datasets such as text, PDFs, images, extracted fields, and metadata.
Experience using metadata, engineered features, embeddings, event or transaction histories, and relationship data to support analytics and modeling.
Strong proficiency in Python for data science and machine learning, including libraries such as Pandas, NumPy, Scikit-learn, PyTorch, or TensorFlow, along with strong SQL skills.
Experience building scalable data processing and machine learning pipelines using AWS-native services and open-source frameworks such as Apache Spark.
Experience integrating models into production environments through APIs, batch pipelines, event-driven workflows, streaming pipelines, or embedded analytics applications.
Understanding of model evaluation, validation, monitoring, drift detection, bias assessment, explainability, and performance measurement.
Understanding of operational challenges in anomaly and fraud detection, including class imbalance, changing patterns, false positives, data leakage, concept drift, and feedback-loop design.
Ability to apply responsible AI, privacy, security, and governance practices when using Generative AI with sensitive or regulated data.
Strong communication skills and the ability to translate analytical outputs, detected risks, and model results into actionable insights.
Experience working in cross-functional, matrixed teams in an Agile environment.

What Would Be Nice To Have:

Experience developing production-grade anomaly detection, fraud detection, waste and abuse analytics, risk-scoring, or investigative decision-support solutions.
Experience using Amazon Bedrock foundation models or comparable enterprise Generative AI capabilities for analytics, anomaly detection, fraud investigation, or case-management workflows.
Experience implementing Retrieval-Augmented Generation (RAG), prompt engineering, agentic workflows, guardrails, foundation-model evaluation, and responsible AI controls for enterprise use cases.
Experience building AI-enabled search solutions, including semantic search, hybrid search, document retrieval, embeddings, and ranking models using AWS-native services.
Experience with multimodal data processing involving text, images, documents, metadata, and other data types.
Familiarity with OCR/ICR and document intelligence pipelines using services such as Amazon Textract and Amazon Bedrock.
Experience using Amazon SageMaker for feature engineering, model development, training, deployment, monitoring, and machine learning lifecycle management.
Experience using Amazon OpenSearch Service, graph databases, or graph-processing frameworks for relationship mapping, link analysis, and suspicious-network detection.
Experience developing explainable AI solutions, including confidence scoring, reason codes, feature attribution, evidence traceability, and analyst-friendly explanations.
Experience designing analytics dashboards, alert-management interfaces, investigative reporting, or case-triage solutions for end users.
Knowledge of AWS security and governance practices, including IAM, encryption, logging, data classification, least-privilege access, and handling sensitive or regulated information.
Previous experience supporting federal clients or working in regulated environments.
Consulting experience or experience in a client-facing delivery role strongly preferred.
Experience supporting training, user enablement, or adoption of analytics capabilities across teams.
Familiarity with graph-based analytics, ontology-driven models, knowledge graphs, or relationship mapping.

#M1
#LI-CD1
Ref: #851-Rockville-S1

Dice Id: 10295162
Position Id: 381091
Posted 30+ days ago

Company Info

About System One

Our dedicated team of recruiters work tirelessly to match jobseekers with careers that align with their skills, aspirations, and goals.

Whether you're an organization seeking customized workforce solutions, outsourced services, or if you're a jobseeker searching for your next opportunity,

System One is the perfect staffing partner for you.

Fitment