Artificial Intelligence Senior Data Scientist

Edgewater Federal Solutions, Inc.•Washington, DC

21h•$134,000 - $139,000•Remote

About The Position

Edgewater Federal Solutions is seeking a highly skilled Senior Data Scientist to support our client as part of its newly established AI Lab. This innovative team is focused on researching, developing, and deploying advanced Artificial Intelligence (AI), Machine Learning (ML), and Generative AI solutions that improve operational efficiency, enhance analytical capabilities, and support the division's consumer protection and community development mission. The Senior Data Scientist will serve as a full-stack AI practitioner responsible for the complete lifecycle of AI/ML solutions—from research and model development through deployment, monitoring, and production support. This role requires hands-on expertise in Generative AI, data science, application development, visualization, cloud deployment, and MLOps practices. The successful candidate will thrive in a collaborative, agile environment and possess the ability to translate complex business challenges into scalable AI-driven solutions. This work can be performed remotely, and does require US Citizenship, this role is for a 6 month long period and not anticipated to be extended.

Requirements

Bachelor's degree in Computer Science, Data Science, Statistics, Machine Learning, Artificial Intelligence, or a related technical discipline.
Minimum of six (6) years of experience developing, deploying, and maintaining AI/ML solutions in enterprise, government, academic, or research environments.
Demonstrated experience owning projects from concept through deployment and operational support.
Experience working independently in highly collaborative cross-functional environments.
Expert-level proficiency in Python and/or R.
Extensive experience with machine learning and statistical modeling frameworks.
Strong experience developing and deploying AI/ML applications in cloud environments.
Experience with: Generative AI technologies and Large Language Models, Prompt engineering and RAG architectures, Application development frameworks (Streamlit, Dash, Flask, R Shiny), Data visualization tools and libraries, Docker and containerized deployments, CI/CD practices and DevOps fundamentals, NLP technologies including Named Entity Recognition (NER), POS tagging, embeddings, and text analytics.
Experience with frameworks such as: Scikit-learn, SpaCy, XGBoost, Other modern AI/ML frameworks.
U.S. Citizenship required.
Ability to successfully obtain and maintain any required Federal Reserve Board background investigation.

Nice To Haves

Master's degree preferred.
Experience supporting federal government, regulatory, or policy-focused organizations.
Experience working with banking, financial services, consumer finance, or regulatory data.
Knowledge of Agile methodologies, Scrum, Kanban, Jira, and Azure DevOps.
Experience with: GPT, Llama, Nova, and similar LLM APIs, LangChain and LlamaIndex, Vector databases and semantic search solutions, Amazon Bedrock, SageMaker, Comprehend, Rekognition, and Transcribe, EC2, ECS, Lambda, S3, CloudWatch, Databricks, Terraform and CloudFormation.
Knowledge of MLOps best practices, automated retraining, monitoring, and model governance.
Experience building production-grade web applications and advanced user interfaces.
Familiarity with explainable AI, fairness evaluation, and model interpretability.
Understanding of multimodal AI technologies.
AWS certifications such as: AWS Solutions Architect, AWS Machine Learning Specialty, Other relevant cloud certifications.
Experience handling sensitive, regulated, or protected data.

Responsibilities

Research, design, and develop Artificial Intelligence and Machine Learning solutions supporting DCCA business objectives.
Build proof-of-concept AI solutions and transition successful prototypes into production environments.
Design and implement applications utilizing Large Language Models (LLMs) for document processing, summarization, classification, workflow automation, and information extraction.
Develop and optimize prompt engineering strategies and Retrieval-Augmented Generation (RAG) architectures.
Evaluate, customize, fine-tune, and validate AI models to improve accuracy and business value.
Apply advanced statistical and machine learning methodologies, including: Supervised and unsupervised learning, Classification and regression modeling, Deep learning techniques, Natural Language Processing (NLP).
Build, deploy, and maintain AI/ML applications across cloud and on-premises environments.
Develop interactive dashboards and analytical applications using frameworks such as Streamlit, Dash, Flask, or R Shiny.
Create compelling visualizations and user interfaces using tools such as Tableau, Power BI, Plotly, Matplotlib, Seaborn, and ggplot2.
Implement CI/CD pipelines, containerization strategies, and automated deployment processes using Docker and related technologies.
Establish monitoring, logging, alerting, model performance tracking, and automated retraining capabilities.
Optimize GenAI deployments, including API management, rate limiting, and cost controls.
Troubleshoot application and infrastructure issues while ensuring system scalability and reliability.
Support security, privacy, compliance, and governance requirements for deployed solutions.
Participate in Agile ceremonies, including sprint planning, standups, and retrospectives.
Collaborate directly with economists, analysts, attorneys, technical teams, and executive leadership.
Translate business requirements into scalable AI and data science solutions.
Communicate technical concepts effectively to both technical and non-technical audiences.
Prepare technical documentation, knowledge transfer materials, reports, and executive briefings.
Mentor team members and contribute to the growth of DCCA's AI/ML capabilities and best practices.
Support compliance with federal governance frameworks, including FISMA, privacy requirements, and records management standards.
Coordinate with security, privacy, compliance, and enterprise technology stakeholders.
Apply Responsible AI principles, including fairness, transparency, interpretability, accountability, and bias mitigation.
Assist with development of security documentation, privacy impact assessments, and Authority to Operate (ATO) packages where required.