Note: The job is a remote job and is open to candidates in USA. Innovee Consulting LLC is establishing an AI Lab to implement generative AI and machine learning solutions. The Senior Data Scientist will support research, development, and deployment of AI/ML solutions to enhance productivity and improve analytical capabilities within the organization.
Responsibilities
- 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
- 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, ••• 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
- 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
- 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
Skills
- 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
- 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
Company Overview