Artificial Intelligence (AI) Senior Data Scientist
Actively Hiring
Full-time Posted 11 days ago
Responsibilities
- check_circle Research, design, and develop machine learning and artificial intelligence solutions to support DCCA's mission, with emphasis on generative AI applications
- check_circle Build and iterate on proof-of-concept AI solutions that demonstrate value for specific use cases, transitioning successful prototypes into production applications
- check_circle Design and implement applications leveraging large language models for text analysis, summarization, information extraction, document classification, and workflow automation
- check_circle Develop prompt engineering strategies and retrieval-augmented generation (RAG) systems to improve AI application performance
- check_circle Experiment with fine-tuning, model customization, and evaluation techniques to optimize AI solutions for DCCA use cases
- check_circle Evaluate emerging AI technologies, frameworks, and models to identify opportunities for adoption within DCCA workflows
- check_circle Apply advanced statistical and machine learning techniques including supervised/unsupervised learning, classification, regression, and deep learning methods
- check_circle 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
- check_circle 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
- check_circle 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
- check_circle Manage deployment pipelines including containerization (Docker), CI/CD practices, and GenAI application deployments with API integrations, rate limits, and cost optimization
- check_circle Implement monitoring, logging, alerting, and visual dashboards for model performance, data quality, and system health; establish automated retraining pipelines and model versioning strategies
- check_circle Troubleshoot and maintain deployed applications, addressing performance issues, ensuring scalability, and updating applications as requirements evolve
- check_circle Support governance requirements including documentation for security assessments, privacy reviews, and compliance obligations related to deployed systems
- check_circle Work within a light agile framework, participating in sprint planning, standups, and retrospectives to coordinate work with team members
- check_circle Break down technical work into manageable tasks, estimate effort, track progress, and communicate status, blockers, and technical challenges to stakeholders
- check_circle 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
- check_circle Communicate technical concepts effectively to both technical and non-technical
- check_circle Document technical work, including code, methodologies, and project outcomes to support knowledge sharing and project continuity
- check_circle Contribute to building an AI/ML practice within DCCA, including documentation, capability development, and mentoring team members
- check_circle Work within federal IT governance frameworks including FISMA, privacy, and records management requirements as they apply to AI systems
- check_circle Coordinate with the Board's security, privacy, and compliance functions on matters related to AI Lab systems and applications
- check_circle Apply responsible AI practices including fairness evaluation, bias detection, model interpretability, and transparency in model development
- check_circle Maintain awareness of AI ethics, accountability, and appropriate use considerations in federal regulatory contexts
- check_circle Support preparation of documentation for system security plans, privacy impact assessments, and authority to operate processes when required
Basic qualifications
- 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
- 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
Tags & Focus Areas
Remote Ai Machine Learning Data Science Generative Ai
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