Scientist, Computational Biology

About the role

MyOme seeks a Scientist, Computational Biology to anchor its early-stage research initiatives. In this capacity, you will own the analytical strategy for translating large-scale human biobanks into clinically relevant risk insights. The position requires a deep partnership between observational epidemiology, omics technologies, and advanced statistical modeling. You will interrogate rich public datasets to discover, evaluate, and validate predictive signatures that identify elevated risk long before clinical manifestation. The role demands intellectual curiosity, technical precision, and a commitment to turning complex data into actionable biological knowledge.

This position is based in Menlo Park, California, and operates as a full-time engagement. The compensation range is $140,000.00 to $199,999.99 annually.

What you'll do

• Meet the bar Practical notes

Key Responsibilities

You will design and implement intake pipelines that process public biobank data, ensuring rigorous quality control for downstream modeling. Your work will focus on plasma proteomics, metabolomics, and genome-wide methylation, preparing these layers for integrated analysis. You will construct analysis workflows using Python or R to interrogate proteomics, metabolomics, and transcriptomics signals across cohort subsets.

A core function is the application of survival methods and longitudinal observational approaches to assess phenotype associations. You will evaluate time-to-event patterns within electronic health record traits to determine biological signals. You will ship proof-of-concept studies that position multi-omic panels against standard clinical risk factors, quantifying their incremental predictive value.

Cross-functional collaboration is central to this role. You will work alongside assay scientists, computational biologists, clinical experts, and product stakeholders to align analytical findings with experimental priorities. You will contribute to the construction of integrative signatures, ensuring robust version control and documentation within cloud-based biobank environments. Your work will directly inform the prioritization of markers for experimental validation, bridging epidemiology and target discovery.

Requirements

You hold a doctorate in a quantitative field such as Computational Biology, Bioinformatics, Biostatistics, Epidemiology, or Human Genetics. Candidates within the first four years post-degree are strongly encouraged to apply; those with a master's degree should bring three to six years of relevant experience.

You possess proven expertise querying and analyzing multi-modal data within major human cohorts, specifically the UK Biobank and the All of Us Research Program. Your analytical portfolio must include work with at least two human data modalities, such as plasma proteomics, metabolomics, genome-wide DNA methylation, and transcriptomics.

You demonstrate strong observational study design skills, including robust confounding control and time-to-event modeling for clinical phenotypes. Your coding skills are production-grade in Python and/or R, with fluency in version control systems like Git. You are experienced working within cloud biobank ecosystems such as DNAnexus, Terra, AWS, or GCP.

You are adept at statistical methods central to biomarker discovery, including strategies for high-dimensional data, hypothesis testing, and regularization. You have hands-on experience with standard machine learning workflows, including ensemble methods and penalized regression. Familiarity with deep learning methodologies is a distinct advantage.

Required Qualifications

You hold a doctorate in a quantitative field such as Computational Biology, Bioinformatics, Biostatistics, Epidemiology, or Human Genetics. Candidates within the first four years post-degree are strongly encouraged to apply; those with a master's degree should bring three to six years of relevant experience.

You possess proven expertise querying and analyzing multi-modal data within major human cohorts, specifically the UK Biobank and the All of Us Research Program. Your analytical portfolio must include work with at least two human data modalities, such as plasma proteomics, metabolomics, genome-wide DNA methylation, or transcriptomics.

You demonstrate strong observational study design skills, including robust confounding control and time-to-event modeling for clinical phenotypes. Your coding skills are production-grade in Python and/or R, with fluency in version control systems like Git. You are experienced working within cloud biobank ecosystems such as DNAnexus, Terra, AWS, or GCP.

You are adept at statistical methods central to biomarker discovery, including strategies for high-dimensional data, hypothesis testing, and regularization. You have hands-on experience with standard machine learning workflows, including ensemble methods and penalized regression. Familiarity with deep learning methodologies is a distinct advantage.

Preferred Qualifications

Experience in building integrated multi-omic risk scores or merging omics data with clinical or genetic information is highly valued. A track record of translating analytical findings into biological insights will differentiate your application.

Practical notes

Full-time employment.

Employment

location: USA

Candidates must be authorized to work in the United States without sponsorship now or at any time during the employment.

Candidates must be physically present in Menlo Park, California, United States, for the role.

Candidates must be authorized to work in the United States without sponsorship now or at any time during the employment.