Data Scientist III – OHS TTA Hub

Location: Remote
Schedule: Full-Time; must be available during core hours of 10:00 a.m.–4:00 p.m. Eastern
Citizenship: U.S. citizenship required
Clearance: Must be able to obtain and maintain a Public Trust clearance and obtain a PIV card
Travel: Occasional travel may be required
Position Status: Contingent upon contract award

Missing Link Security is seeking an experienced Data Scientist III to support the Office of Head Start (OHS) Training and Technical Assistance Data Platform, known as the TTA Hub, in support of the U.S. Department of Health and Human Services (HHS), Administration for Children and Families (ACF).

The Data Scientist III will develop and validate advanced analytics and, when prioritized, artificial intelligence capabilities that help OHS better understand training and technical assistance needs and outcomes. This role will apply rigorous statistical and data science methods while ensuring that models and analytical approaches are explainable, validated, reproducible, and appropriately monitored.

This is a Key Personnel position and will be named in our proposal.

Responsibilities

• Lead development of statistical, machine learning, and, when prioritized, AI models supporting TTA reporting, trend analysis, and actionable insights.
• Ensure each AI use case is appropriately documented and explainable.
• Define expected outcomes, baselines, experimental designs, and validation approaches before deployment.
• Establish methods for evaluating uncertainty and monitoring model performance.
• Develop and maintain appropriate model documentation.
• Partner with the Senior Data Analyst on data definitions, data quality, and responsible interpretation of analytical results.
• Communicate analytical findings and their implications clearly to program leaders.
• Prototype and productionize approved analytical and modeling approaches in collaboration with engineering and security team members.
• Develop and apply tests addressing data leakage, bias, privacy, and performance.
• Promote reproducibility and appropriate validation throughout the analytics and modeling lifecycle.