Contract Performance Period : August 3, 2026 - December 31, 2026
Job Location : Washington, DC location(s) (Metro Access) remote.
Direct Hire : Term: through 31/12/2026, with a strong possibility of extension
Pay Range : Depending on Experience
Travel Requirements : Telework available
Working Remotely :
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 :
Skills Requirements :
Responsibilities :
Job ID : 1577
Job Location : Washington, DC location(s) (Metro Access) remote.
Direct Hire : Term: through 31/12/2026, with a strong possibility of extension
Pay Range : Depending on Experience
Travel Requirements : Telework available
Working Remotely :
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
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