SPECTRA AI Modeling and Federated Data Platforms (TA2) SETA

Strategic Analysis, Inc. is an Equal Opportunity Employer.





SPECTRA AI Modeling and Federated Data Platforms (TA2) SETA (#26-166)

Job Code:26-166
Location:Remote
FT/PT Status:Full Time
Required Clearance:Public Trust
Salary Range:$150k-$200k

Strategic Analysis, Inc. (SA) is actively seeking a SPECTRA AI Modeling and Federated Data Platforms (TA2) SETA 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.

Program Overview
ARPA-H's SPECTRA program (Systems for Phenotypic Evaluation, Clinical Trajectories, Response, and Agency) aims to change how autism is understood and supported. It grounds research in objective, multi-factor measurement and computation instead of behavioral observation alone. By combining data across the lifespan, SPECTRA aims to match each person to the interventions most likely to help, and to keep improving care and outcomes over time (ARPA-H, n.d.-b).

SPECTRA has four connected technical areas (TAs):

• TA1 – Analysis: tools that link genetic, environmental, family, metabolic, immune, and developmental factors to different paths in development, health, communication, and daily function.
• TA2 – Modeling/Integration: AI platforms that map those paths across many sites while keeping data private, and identify factors that can be changed to improve outcomes.
• TA3 – Precision Intervention: choosing and rigorously testing interventions matched to each person.
• TA4 – Enabling Technologies: accessible tools for communication, quality of life, and embedded assessment that measure real-world function through ordinary use.

This SETA position supports TA2, the program's integration hub. TA2 platforms will learn from biological and environmental data from TA1 and continuous real-world data from TA4 (ARPA-H, 2026). They do this by sharing models across institutions, so raw data never leaves its home site. The resulting models should separate factors that actually change outcomes from those that are only correlated. Their outputs will help TA3 choose and test interventions for each person.

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 data: 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.

Strategic Analysis, Inc. is an Equal Opportunity employer and is committed to non-discrimination in employment. All qualified applicants will receive consideration for employment without regard to race, color, religions, sex (including pregnancy, sexual orientation, or gender identity), national origin, disability (physical or mental), age (40 or older), protected veteran status, genetic information (including family medical history) or any other characteristic protected by law. This policy includes but is not limited to the following employment actions: recruitment, hiring, firing, promotion, demotion, compensation, fringe benefits, training, mentoring and sponsorship programs.