Senior AI Data Scientist

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