Senior Data Scientist (AI

Senior Data Scientist (AI)

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 productivity, improve analytical capabilities, and strengthen the Division's work in consumer protection and community development.

We are looking for a full-stack Senior Data Scientist to support the AI Lab's research, development, and implementation of AI/ML solutions, with emphasis on generative AI applications. This role requires 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-prem and/or cloud infrastructure.

The AI Lab operates as a small, agile team where practitioners are expected to move between research, development, and deployment activities. This position will contribute to strategy while doing hands-on technical work, building models, training systems, evaluating performance, and deploying solutions. The AI Lab collaborates closely with DCCA's Data Analytics and Risk and Surveillance sections, and coordinates with the Board's enterprise technology on infrastructure, governance, and compliance matters.

• U.S. citizenship
• At least six 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 technology field (Master's degree preferred)
• Expert proficiency in Python or R for data science development; experience with additional programming languages
• Production deployment experience:
  Demonstrated ability to build, deploy, and maintain AI/ML applications in cloud environments, including containerization and basic CI/CD practices
• Application development:
  Proficiency building interactive applications and dashboards using frameworks such as Streamlit, Dash, Flask, RShiny, or similar
• Data visualization:
  Strong experience creating visualizations and dashboards using Python/R libraries, Tableau, Power BI, or similar tools to communicate technical concepts to non-technical audiences
• AI/ML expertise:
  Advanced knowledge of machine learning, NLP (text normalization, Named Entity Recognition, POS tagging, word embeddings), and Generative AI technologies; experience with frameworks such as Scikit-learn, Spacy, XGBoost
• Statistical analysis:
  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

AI/ML and Generative AI Development:

• Research, design, and develop machine learning and artificial intelligence solutions to support DCCA's mission, with emphasis on generative AI applications
• Build and iterate on proof-of-concept AI solutions that demonstrate value for specific use cases, transitioning successful prototypes into production applications
• Design and implement applications leveraging large language models 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
• Experiment with fine-tuning, model customization, and evaluation techniques to optimize AI solutions for DCCA use cases
• Evaluate emerging AI technologies, frameworks, and models to identify opportunities for adoption within DCCA workflows
• Apply advanced statistical and machine learning techniques including supervised/unsupervised learning, classification, regression, and deep learning methods

Deployment and Operations:

• Build, deploy, and maintain AI/ML models and applications in cloud environments (AWS, Kubernetes, or internal analytics platforms), working collaboratively with AI Cloud Engineers when available or independently managing end-to-end deployment
• Develop interactive dashboards and analytical applications using Python frameworks (Streamlit, Dash, Flask) or R Shiny; leverage AI-assisted development tools to rapidly prototype and iterate on data products
• Create data visualizations and user interfaces using Python libraries (Plotly, Matplotlib, Seaborn), R (ggplot2), Tableau, Power BI, or similar tools that translate analytical outputs into intuitive, actionable insights for non-technical audiences
• Manage deployment pipelines including containerization (Docker), CI/CD practices, and GenAI application deployments with API integrations, rate limits, and cost optimization
• Implement monitoring, logging, alerting, and visual dashboards for model performance, data quality, and system health; establish automated retraining pipelines and model versioning…