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Skills

Productivity
IaaS
Research
Computer Science
Statistics
Data Science
Programming Languages
Natural Language Processing
Named-Entity Recognition (NER)
Point Of Sale
scikit-learn
XGBoost
Statistical Models
Data Analysis
Problem Solving
Conflict Resolution
Application Development
Data Visualization
Plotly
matplotlib
Tableau
Microsoft Power BI
LangChain
LlamaIndex
Vector Databases
Semantic Search
Amazon SageMaker
Amazon EC2
Amazon S3
Terraform
Machine Learning Operations (ML Ops)
Agile
JIRA
Microsoft Azure
DevOps
IT Governance
FISMA
Software Security
Research Design
Prototyping
Large Language Models (LLMs)
Extraction
Workflow
Prompt Engineering
Cloud Computing
Amazon Web Services
Kubernetes
Analytical Skill
Python
Dash Python
Flask
R
Management
Docker
Continuous Integration
Continuous Delivery
Generative Artificial Intelligence (AI)
API
Optimization
Dashboard
Data Quality
Presentations
Evaluation
System Security
Privacy
Artificial Intelligence
Machine Learning (ML)
Documentation
Mentorship

Productivity
IaaS
Research
Computer Science
Statistics
Data Science
Programming Languages
Natural Language Processing
Named-Entity Recognition (NER)
Point Of Sale
scikit-learn
XGBoost
Statistical Models
Data Analysis
Problem Solving
Conflict Resolution
Application Development
Data Visualization
Plotly
matplotlib
Tableau
Microsoft Power BI
LangChain
LlamaIndex
Vector Databases
Semantic Search
Amazon SageMaker
Amazon EC2
Amazon S3
Terraform
Machine Learning Operations (ML Ops)
Agile
JIRA
Microsoft Azure
DevOps
IT Governance
FISMA
Software Security
Research Design
Prototyping
Large Language Models (LLMs)
Extraction
Workflow
Prompt Engineering
Cloud Computing
Amazon Web Services
Kubernetes
Analytical Skill
Python
Dash Python
Flask
R
Management
Docker
Continuous Integration
Continuous Delivery
Generative Artificial Intelligence (AI)
API
Optimization
Dashboard
Data Quality
Presentations
Evaluation
System Security
Privacy
Artificial Intelligence
Machine Learning (ML)
Documentation
Mentorship

Summary

Project Description :
We are seeking a Senior Data Scientist to support an AI Lab focused on exploring and implementing generative AI and machine learning solutions that enhance staff productivity and improve analytical capabilities. This is a full-stack role requiring end-to-end ownership - from exploratory research and model development through application deployment and production maintenance.

The ideal candidate is comfortable working across the full technology stack: building models, creating visualizations, developing applications, and deploying solutions to on-premises and/or cloud infrastructure. The AI Lab operates as a small, agile team where practitioners move fluidly between research, development, and deployment activities.

Location: Washington, DC | US Citizenship is Required

Qualification Requirements :

US Citizenship required
Minimum 6 years of hands-on experience developing, deploying, and maintaining AI/ML applications within a large professional or academic organization
Bachelor's degree in Computer Science, Data Science, Statistics, Machine Learning, or related field (Master's degree preferred)
Expert proficiency in Python or R for data science development; experience with additional programming languages a plus
Production deployment experience: ability to build, deploy, and maintain AI/ML applications in cloud environments, including containerization and basic CI/CD practices
Proficiency building interactive applications and dashboards using frameworks such as Streamlit, Dash, Flask, or R Shiny
Strong experience creating visualizations and dashboards using Python/R libraries, Tableau, Power BI, or similar tools
Advanced knowledge of machine learning, NLP (Named Entity Recognition, POS tagging, word embeddings), and Generative AI technologies; experience with Scikit-learn, SpaCy, XGBoost
Advanced knowledge of statistical modeling, data analysis techniques, and problem-solving skills
Ability to work independently and collaboratively, taking ownership of solutions from conception through production deployment

Skills Requirements :

Generative AI & LLM application development: prompt engineering, RAG systems, fine-tuning, model evaluation
Cloud deployment: AWS, Kubernetes, containerization (Docker), CI/CD pipelines
Application frameworks: Streamlit, Dash, Flask, R Shiny
Data visualization: Plotly, Matplotlib, Seaborn, ggplot2, Tableau, Power BI
LLM APIs and frameworks: GPT, Llama, LangChain, LlamaIndex; vector databases and semantic search
AWS AI services: Amazon Bedrock, SageMaker, Comprehend, Rekognition, Transcribe
AWS deployment services: EC2, ECS, Lambda, S3, CloudWatch
Infrastructure as code: Terraform, CloudFormation
MLOps practices: model monitoring, versioning, automated retraining, and deployment pipelines
Responsible AI practices: bias detection, fairness evaluation, and model interpretability
Agile project tracking tools: Jira, Azure DevOps
Federal IT governance frameworks: FISMA, privacy requirements, and application security in regulated environments

Responsibilities :

Research, design, and develop machine learning and generative AI solutions, including proof-of-concept prototypes transitioning into production applications
Design and implement applications leveraging large language models (LLMs) for text analysis, summarization, information extraction, document classification, and workflow automation
Develop prompt engineering strategies and retrieval-augmented generation (RAG) systems to improve AI application performance
Build, deploy, and maintain AI/ML models in cloud environments (AWS, Kubernetes), managing end-to-end deployment independently or collaboratively
Develop interactive dashboards and analytical applications using Python frameworks (Streamlit, Dash, Flask) or R Shiny
Manage deployment pipelines including containerization (Docker), CI/CD practices, and GenAI API integrations with cost optimization
Implement monitoring, logging, alerting, and dashboards for model performance, data quality, and system health
Communicate technical concepts effectively to both technical and non-technical audiences through presentations, reports, and executive summaries
Apply responsible AI practices including fairness evaluation, bias detection, and model interpretability
Support governance documentation including system security plans, privacy impact assessments, and authority to operate processes
Contribute to building an AI/ML practice through documentation, capability development, and mentoring team members

Job ID : 1577

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: 10301578
Position Id: 1577
Posted 17 hours ago

Senior Data Scientist (AI)

Washington, District of Columbia

•

Yesterday

Senior Data Scientist (AI) Washington, DC onsite presence highly preferred Period of Performance: 6 months Per Federal contract U.S. Citizenship Required Must be able to pass enhanced background screen (criminal, financial, drug) for Public Trust clearance W-2 or C2C Application Deadline: 7/27/2026 The Federal Reserve Board''s Division of Consumer and Community Affairs (DCCA) is establishing an AI Lab to explore and implement generative AI and machine learning solutions that enhance staff pro

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