Description:

Position Summary
The client'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 client'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 client's  enterprise technology on infrastructure, governance, and compliance matters.

Responsibilities
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 strategies
Troubleshoot and maintain deployed applications, addressing performance issues, ensuring scalability, and updating applications as requirements evolve
Support governance requirements including documentation for security assessments, privacy reviews, and compliance obligations related to deployed systems

Collaboration, Communication and Agile Practices

Work within a light agile framework, participating in sprint planning, standups, and retrospectives to coordinate work with team members
Break down technical work into manageable tasks, estimate effort, track progress, and communicate status, blockers, and technical challenges to stakeholders
Work directly with DCCA program staff economists, analysts, attorneys, and senior leadership to understand business needs, identify AI/ML opportunities, and translate requirements into technical solutions
Communicate technical concepts effectively to both technical and non-technical audiences through presentations, reports, and executive summaries
Document technical work, including code, methodologies, and project outcomes to support knowledge sharing and project continuity
Contribute to building an AI/ML practice within DCCA, including documentation, capability development, and mentoring team members

Governance and Compliance Awareness

Work within federal IT governance frameworks including FISMA, privacy, and records management requirements as they apply to AI systems
Coordinate with the Board's security, privacy, and compliance functions on matters related to AI Lab systems and applications
Apply responsible AI practices including fairness evaluation, bias detection, model interpretability, and transparency in model development
Maintain awareness of AI ethics, accountability, and appropriate use considerations in federal regulatory contexts
Support preparation of documentation for system security plans, privacy impact assessments, and authority to operate processes when required

Required Qualifications

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

Preferred Qualifications

Prior experience in U.S. federal government, regulatory, supervisory, or policy environments
Experience with financial services data, consumer finance, banking supervision, or regulatory data
Experience working within agile frameworks (Scrum, Kanban) and project tracking tools (Jira, Azure DevOps)
Experience with LLM APIs (GPT, Llama, Nova) and frameworks (LangChain, LlamaIndex); knowledge of prompt engineering, fine-tuning, vector databases, and semantic search
Familiarity with AWS AI services (Amazon Bedrock, SageMaker, Comprehend, Rekognition, Transcribe)
Experience building production-grade web applications with advanced user interfaces; knowledge of data storytelling and visual design principles
Experience visualizing model performance metrics, feature importance, and model explainability outputs
Hands-on experience with AWS deployment services (EC2, ECS, Lambda, S3,CloudWatch), Databricks, and infrastructure as code (Terraform, CloudFormation)
AWS certifications (Solutions Architect, Machine Learning Specialty, or similar)
Familiarity with MLOps practices including model monitoring, versioning, automated retraining, and deployment pipelines
Experience with multi-modal AI applications; understanding of responsible AI practices (bias detection, fairness evaluation, model interpretability)
Familiarity with federal IT governance frameworks (FISMA, privacy requirements) and application security in regulated environments
Experience working with sensitive or regulated data