Multi-Factor Analysis (Genomics, 'Omics and Computational Biology) SETA (#26-167)

Strategic Analysis, Inc. (SA) is actively seeking an Multi-Factor Analysis SETA with subject matter expertise in Genomics, 'Omics and Computational Biology. 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 how to characterize the factors behind different autism trajectories. These include genetics and epigenetics, metabolomics, immune profiling, the microbiome, environmental exposures, family factors, and developmental history. The program's starting idea is that nonlinear interactions among these factors shape different developmental, health, communication, and functional outcomes (ARPA-H). Give insight on whether proposed approaches are conceptually and technically sound: Study design: cohort design, sample size, and statistical power Measurement: assay and measurement choices, and batch effects Data handling: combining data from different sources, and quality control Analysis: reproducibility, and statistical methods for gene-environment and other multi-factor interactions Scope: any justifiable way of measuring a person's biological state is in scope, including neuroimaging and electrophysiology. Characterization is focused on humans, not model systems such as organoids (ARPA-H, n.d.-b). Help set goals, expectations, and budgets for TA1 projects, including milestones for generating, harmonizing, and sharing data. Help review and oversee funded TA1 projects, including: coordinating with TA2 so TA1 data and features can feed the federated, privacy-preserving modeling platforms (ARPA-H) coordinating with TA3 so TA1 findings point to targets that interventions can act on Experience/Expertise Required: Experience in human genetics/genomics or other 'omics research (e.g., sequencing, epigenomics, transcriptomics, proteomics, metabolomics, microbiome), covering both how the data are generated and how they're analyzed Experience with computational methods for high-dimensional biological bioinformatics, statistical genetics, data science, computational modeling, and/or machine learning Experience working with large human cohort or long-term datasets that combine several data types Experience with budgeting and program or project management Experience/Expertise Preferred: Minimum five years of experience post-doc. Genetics or biology research on autism or other neurodevelopmental conditions Familiarity with large autism and population datasets (e.g., SPARK, Simons Simplex Collection, MSSNG, ABCD, All of Us) Environmental epidemiology or exposome science; immunology; metabolism; gut-brain or microbiome research Methods for causal inference and interactions (e.g., Mendelian randomization, gene-by-environment modeling, polygenic scores, multi-omic integration, subtyping or clustering patients by trajectory) Neuroimaging or EEG analysis Data standards, governance, and privacy for genomic data, including FAIR principles, GA4GH standards, consent, and assent for minors, and returning results to participants Engaging communities in genetic research, and awareness of autistic community perspectives on it Lived experience of autism is a plus. Disclosure is voluntary and not required. Minimum Degree Requirement: PhD (or MD/PhD) in human genetics, genomics, bioinformatics, computational biology, biostatistics, epidemiology, systems biology, or a related field. A master’s degree with substantial relevant research experience may be considered.