AI Modeling and Federated Data Platforms SETA (#26-166)

Strategic Analysis, Inc.; Arlington (VA); Remote; USD 120,000 - 190,000; Full time

Job summary

Strategic Analysis, Inc. (SA) seeks an AI Modeling and Federated Data Platforms expert to guide federated and privacy-preserving AI efforts for health data within the SETA program.

The role focuses on technical guidance, oversight, and ensuring sound methodologies across longitudinal trajectories and causal inference. Requirements include a PhD (or substantial equivalent) and at least five years post-doctoral experience, with expertise in federated learning, privacy technologies, and multi-site

Qualifications

• PhD in a relevant field; substantial research/industry experience may substitute.
• Minimum five years post-doc experience.
• Experience with federated or distributed learning and privacy-preserving computation.

Responsibilities

• Provide SME guidance on federated and privacy-preserving AI for health data across models and trajectories.
• Assess technical soundness of proposed federated architectures and privacy methods.
• Assist in budgeting, milestones, and goals for TA2 projects and cross-program integration.
• Review and oversee TA2 projects including data integration across TA4 and TA1 streams.

Skills

Federated learning

Privacy-preserving ML

Longitudinal modeling

Causal inference

Budgeting & program management

Education

PhD in CS/ML/biostatistics/biomedical informatics

Tools

OHDSI

PCORnet

N3C

OMOP CDM

HL7 FHIR

GA4GH

Job description

Strategic Analysis, Inc. (SA) is actively seeking an AI Modeling and Federated Data Platforms with subject matter expertise in federated and privacy-preserving AI for health data. This is a Science and Engineering and Technical Advisor (SETA) role, focused on providing technical guidance and oversight.

Specific Duties Required:

• Provide internal subject matter expertise on federated and privacy-preserving AI for health data. Cover modeling developmental, health, communication, and functional trajectories across the lifespan. Also cover methods for finding outcome drivers that can be changed and targets that interventions can act on (ARPA-H).
• Give insight on whether proposed approaches are conceptually and technically sound:
  • Federated architecture: federated learning and analytics across institutions and settings without moving raw data
  • Privacy: privacy-enhancing technologies, such as differential privacy, secure multiparty computation, homomorphic encryption, and trusted execution environments
  • Modeling: longitudinal and trajectory modeling, and finding subgroups of people with different trajectories
  • Causal inference: telling modifiable drivers apart from correlations, including counterfactual methods and emulating target trials
  • Multimodal combining TA1 data (genetic, 'omics, environmental, clinical) with TA4 real-world data (sensor, audio, video, language, school-based measures)
  • Trustworthiness: validation, generalizability, bias and fairness across age, sex, race and ethnicity, and support needs, plus interpretability and uncertainty
• Help set goals, expectations, and budgets for TA2 projects, including milestones for the platform, model performance, data onboarding, and interoperability.
• Help review and oversee funded TA2 projects. That includes integration across the program:
  • bringing TA4 data streams into TA2 frameworks (ARPA-H)
  • bringing in TA1 data
  • providing model outputs that help TA3 choose and test interventions for each person

Experience/Expertise Required:

• Minimum five years of experience post-doc.
• Experience developing or evaluating machine learning and computational models with real-world health or biomedical data
• Experience with federated or distributed learning, or with other privacy-preserving computation on sensitive data
• Experience with longitudinal or causal modeling methods (e.g., trajectory or mixed-effects models, survival analysis, causal inference, causal ML)
• Experience with budgeting and program or project management

Experience/Expertise Preferred:

• Experience building or running multi-site research data networks (e.g., OHDSI, PCORnet, N3C, or federated genomics consortia)
• Data standards and interoperability (e.g., OMOP CDM, HL7 FHIR, GA4GH)
• Multimodal and foundation-model approaches for combining sensor, audio, video, language and 'omics data
• Familiarity with health data governance and privacy requirements (HIPAA, and FERPA for school data), consent and assent for minors, and data use agreements
• Responsible AI frameworks and regulatory context (e.g., NIST AI RMF, FDA guidance on AI-enabled medical devices)
• Engineering for scalable platforms: cloud and high-performance computing, MLOps, security
• Autism or neurodevelopmental data and research
• Participatory or community data governance, i.e., giving autistic people and families a say in how their data and models are used
• Lived experience of autism is a plus. Disclosure is voluntary and not required.

Minimum Degree Requirement:

• PhD in computer science, machine learning, statistics or biostatistics, biomedical informatics, applied mathematics, electrical engineering, or a related field. A master's degree with substantial relevant research or industry experience may be considered.

Location:

Position is remote.

Travel:

Periodic travel may be required

Clearance:

Ability to obtain HHS Public Trust.

Equal employment opportunity, including veterans and individuals with disabilities.