AI Data Scientist
RemoteUnited States
Job Summary
Develop, train, and deploy machine learning models, predictive analytics solutions, and statistical frameworks to support HUD OCIO AI/ML proofs of concept and pilots. Design simulations, forecasting models, and decision-support systems while conducting feature engineering and data preparation to identify patterns and risks. Collaborate with engineering and DevOps teams to operationalize advanced analytics, integrate large language models into enterprise workflows, and manage MLOps practices. Architect the Enterprise AI Security Platform, build scalable data pipelines, and ensure compliance with responsible AI principles and cybersecurity standards. Present findings to stakeholders and mentor junior team members. This remote position requires a federal background check, NACI clearance, and occasional U.S. travel.
Required Qualifications
- federal background check
- NACI clearance
- reside in the United States
- 10-15% annual travel within the U.S.
- deep technical expertise in data science
- machine learning
- statistical modeling
- ability to translate complex business problems into scalable, production-ready solutions
- work across multidisciplinary teams including engineering, AI architects, security professionals, DevOps teams, and business stakeholders
- influence enterprise-scale AI initiatives
- develop advanced decision-support solutions
- help establish best practices for responsible AI, model governance, and data-driven innovation
- Develop, train, validate, and deploy machine learning models, predictive analytics solutions, and statistical frameworks
- Design and implement simulations, forecasting models, optimization techniques, and decision-support systems
- Analyze structured and unstructured datasets to identify patterns, trends, risks, and opportunities
- Conduct feature engineering, data preparation, cleansing, transformation, and quality assessments
- Evaluate model performance and continuously refine algorithms to improve accuracy, reliability, and business impact
- Support the development of AI-enabled solutions utilizing machine learning, generative AI, large language models (LLMs), and emerging AI technologies
- Collaborate with AI engineering teams to operationalize models and integrate them into enterprise applications and workflows
- Develop analytical methodologies for risk detection, anomaly identification, behavioral analysis, and decision intelligence
- Participate in AI experimentation, proof-of-concept development, pilot initiatives, and production deployments
- Architect the target-state Enterprise AI Security Platform, including platform components, security services, data flows, APIs, integration patterns, trust boundaries, and deployment models
- Partner with engineering teams to build scalable data pipelines and analytics workflows
- Work with enterprise datasets across SQL, NoSQL, cloud-native platforms, and distributed data environments
- Contribute to MLOps practices, reproducible workflows, version-controlled development, and model lifecycle management
- Support integration of machine learning solutions with APIs, cloud services, data platforms, and business applications
- Document models, assumptions, methodologies, testing procedures, and decision logic
- Maintain reproducible and auditable workflows to support governance, compliance, and operational excellence
- Ensure compliance with responsible AI principles, privacy requirements, cybersecurity standards, and ethical AI practices
- Contribute to model governance, validation frameworks, and risk management activities
- Collaborate with stakeholders, architects, engineers, UX teams, and business leaders to define analytical requirements and success metrics
- Present findings, recommendations, and technical concepts to both technical and non-technical audiences
- Mentor junior team members and contribute to the development of data science best practices and standards
Hiring someone like this?
Get your role in front of qualified candidates on Sorce.