Senior Scientist

Convergenz; United States; On-site; USD 120,000 - 180,000; Full time

Job summary

Convergenz seeks a Senior Scientist in Genomics & Computational Biology to lead investigations into autism trajectories through genetics, epigenomics, metabolomics, and neuroimaging data. You will guide study design, measurements, and data integration across multi-cohort datasets.

The role emphasizes advanced data analysis, reproducible methods, and collaboration with cross-functional teams to inform budgets and project milestones for translational research.

Qualifications

• Experience in human genetics/genomics or other omics research (sequencing, epigenomics, transcriptomics, proteomics, metabolomics, microbiome) is required.
• Experience with computational methods for high-dimensional biological data: bioinformatics, statistical genetics, data science, computational modeling, and/or machine learning.
• Experience with large human cohorts or long-term datasets combining several data types.
• Experience with budgeting and program or project management.

Responsibilities

• Provide internal subject matter expertise on factors behind autism trajectories including genetics, epigenetics, metabolomics, immune profiling, microbiome, environmental exposures, and developmental history.
• Assess conceptual and technical soundness of proposed approaches; advise on study design, measurement choices, batch effects, and data handling.
• Help set goals, budgets, milestones for TA1 projects and coordinate with TA2/TA3 for data integration and target identification.

Skills

Genomics

Bioinformatics

Data science

Machine learning

Cohort analysis

Budgeting

Project management

Education

PhD in human genetics/genomics or related

Master’s degree with substantial relevant research

Job description

Senior Scientist – Genomics & Computational Biology

Roles and Responsibilities:

• 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
• 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
• 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
• coordinating with TA3 so TA1 findings point to targets that interventions can act on
• Requirements:
• 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 data: 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

Preferred Experience:

• 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: 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