Scientist, Computational Biology

MyOme•Menlo Park, CA

3h•$130,000 - $150,000•Hybrid

About The Position

MyOme’s mission is to provide clinically actionable genetic information to patients throughout their lives. We combine clinical-grade whole genome sequencing, advanced AI methods for genome interpretation, and seamless digital tools for doctors and patients to order and access results. Our team is composed of seasoned entrepreneurs, scientists, and operators, and we're backed by top-tier investors. We are seeking a driven, analytical Scientist, Computational Biology to join our early-stage research team. In this role, you will leverage large-scale, publicly available human biobanks to evaluate the feasibility of multi-omic disease risk prediction. You will sit at the intersection of observational epidemiology, high-throughput omics, and statistical modeling. You will mine rich human datasets to discover, evaluate, and validate multi-omic signatures that identify individuals at risk before clinical onset.

Requirements

Ph.D. in Computational Biology, Bioinformatics, Biostatistics, Epidemiology, Human Genetics, or a related quantitative field with 0–4 years of experience (or Master’s degree with 3–6+ years of relevant experience).
Proven hands-on experience querying and analyzing multi-modal data in major human cohorts, specifically UK Biobank and/or All of Us.
Demonstrated experience analyzing at least two of the following human data types: Plasma proteomics, Metabolomics, Genome-wide DNA methylation (array or sequencing), Bulk/single-cell transcriptomics.
Strong background in observational study design, association testing, confounding control, and time-to-event modeling on clinical/EHR phenotypes.
High proficiency in Python and/or R, version control (Git), and working in cloud-based biobank environments (e.g., DNAnexus, Terra, AWS, or GCP).
Well-versed in statistical approaches applicable to biomarker discovery (e.g., high-dimensional feature selection, hypothesis testing, regularization) and experienced with standard machine learning workflows (e.g., random forests, gradient boosting, penalized regression).

Nice To Haves

Hands-on experience with deep learning methodologies is preferred.
Experience constructing integrated multi-omic risk scores or combining omics with Polygenic Risk Scores (PRS).
Familiarity with causal inference methods (e.g., Mendelian Randomization).
Experience working in an agile, early-stage biotech startup environment.

Responsibilities

Ingest, process, and perform quality control on large-scale public human datasets, with an emphasis on the UK Biobank and the All of Us Research Program.
Analyze complex human molecular profiling data, drawing from modalities such as plasma proteomics (e.g., Olink, SomaScan), metabolomics, genome-wide DNA methylation, and transcriptomics.
Apply biostatistical, observational, and machine learning methods (e.g., survival analysis, Cox models, longitudinal trajectory analysis) to evaluate phenotype associations and early disease risk.
Design and execute fast computational experiments to determine whether specific multi-omic panels add incremental predictive value over standard clinical risk factors or polygenic risk scores.
Partner closely with computational scientists, assay scientists, clinical and regulatory experts, and product development stakeholders to communicate analytical findings and help prioritize targets/markers for experimental validation.