Senior Data Scientist - Artificial Intelligence

Diverse Agile Solutions•Washington, DC

6d•Hybrid

About The Position

Diverse Agile Solutions (DAS) is seeking a Senior Data Scientist (Artificial Intelligence) to support the Federal Reserve Board's Division of Consumer and Community Affairs (DCCA) as part of its newly established AI Lab. This role involves building next-generation Artificial Intelligence capabilities using Generative AI and Machine Learning to enhance consumer protection, regulatory oversight, community development, and operational efficiency. The ideal candidate will be a full-stack AI practitioner responsible for the entire AI lifecycle, from research and development to deployment and monitoring. You will collaborate with economists, attorneys, analysts, and senior leadership to create intelligent solutions that provide significant business value.

Requirements

U.S. Citizenship
Bachelor's degree in Computer Science, Data Science, Statistics, Machine Learning, or a related field
Minimum 6 years of professional experience developing, deploying, and maintaining AI/ML applications
Expert proficiency with Python or R
Experience building production AI applications
Experience deploying Machine Learning solutions into cloud environments
Strong knowledge of: Machine Learning, Deep Learning, Natural Language Processing, Generative AI
Experience with: Scikit-learn, spaCy, XGBoost
Experience developing interactive applications using: Streamlit, Dash, Flask, R Shiny
Experience creating dashboards and visualizations using: Tableau, Power BI, Plotly, Matplotlib, Seaborn
Experience with containerization and CI/CD
Strong statistical modeling and analytical skills
Excellent written and verbal communication skills
Ability to independently deliver solutions from concept through production

Nice To Haves

Master's degree preferred
Federal Government or Regulatory Agency experience
Financial Services or Banking experience
Consumer Finance experience
Banking Supervision experience
Agile methodologies (Scrum, Kanban)
Jira or Azure DevOps experience
Large Language Models (GPT, Llama, Nova)
LangChain experience
LlamaIndex experience
Prompt Engineering experience
Vector Databases experience
Semantic Search experience
AWS AI Services including: Amazon Bedrock, SageMaker, Comprehend, Rekognition, Transcribe
AWS deployment services: EC2, ECS, Lambda, S3, CloudWatch
Databricks experience
Infrastructure as Code: Terraform, CloudFormation
MLOps experience
Model Monitoring experience
Automated Retraining experience
Responsible AI experience
AI Explainability experience
Multi-modal AI experience
AWS Certifications
Experience handling regulated or sensitive data

Responsibilities

Research, design, and develop innovative AI and Machine Learning solutions supporting DCCA initiatives.
Build proof-of-concept AI applications and transition successful prototypes into production.
Develop Generative AI solutions using Large Language Models (LLMs).
Design Retrieval-Augmented Generation (RAG) architectures.
Implement prompt engineering strategies for enterprise AI applications.
Fine-tune and evaluate foundation models for domain-specific use cases.
Develop NLP solutions including: Text classification, Named Entity Recognition (NER), Information extraction, Document summarization, Semantic search.
Apply supervised, unsupervised, deep learning, and statistical modeling techniques.
Evaluate emerging AI frameworks and technologies for enterprise adoption.
Build production-ready AI applications using Python, Streamlit, Dash, Flask, or R Shiny.
Develop intuitive dashboards and visual analytics applications.
Create interactive data visualizations using Plotly, Matplotlib, Seaborn, Tableau, or Power BI.
Translate complex analytical findings into actionable business insights.
Deploy AI and ML applications into cloud environments.
Containerize applications using Docker.
Build CI/CD pipelines for AI deployments.
Implement model monitoring and observability.
Create automated retraining pipelines.
Manage model versioning and lifecycle management.
Optimize API integrations and AI inference costs.
Troubleshoot and maintain production AI applications.
Collaborate with Cloud Engineers and Infrastructure teams.
Participate in Agile ceremonies including: Sprint Planning, Daily Standups, Sprint Reviews, Retrospectives.
Partner with business stakeholders to identify AI opportunities.
Translate business requirements into scalable AI solutions.
Present findings to executive leadership and technical teams.
Document code, methodologies, and technical decisions.
Mentor team members and contribute to the growth of DCCA's AI practice.
Develop AI solutions aligned with federal security and governance standards.
Support FISMA, Privacy Impact Assessments, and ATO documentation.
Apply Responsible AI principles including: Fairness, Bias detection, Explainability, Transparency.
Collaborate with security and compliance teams throughout the AI lifecycle.