What are the responsibilities and job description for the Scientist, Computational Biology position at MyOme?
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.
Position Overview:
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.
What You'll Do:
Benefits:
MyOme values diversity in all forms. We believe that diverse perspectives drive better science and better patient outcomes. We are an Equal Opportunity Employer committed to creating an inclusive workplace that empowers every individual.
Why Work at MyOme?
Join us if you:
Position Overview:
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.
What You'll Do:
- Biobank Data Mining: 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.
- Multi-Omics Analysis: Analyze complex human molecular profiling data, drawing from modalities such as plasma proteomics (e.g., Olink, SomaScan), metabolomics, genome-wide DNA methylation, and transcriptomics.
- Risk Prediction & Observational Epidemiology: Apply biostatistical, observational, and machine learning methods (e.g., survival analysis, Cox models, longitudinal trajectory analysis) to evaluate phenotype associations and early disease risk.
- Feasibility & Proof-of-Concept: 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.
- Cross-Functional Collaboration: 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.
- Education & Experience:
- 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).
- Biobank Expertise: Proven hands-on experience querying and analyzing multi-modal data in major human cohorts, specifically UK Biobank and/or All of Us.
- Omic Proficiency: 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
- Epidemiological & Statistical Rigor: Strong background in observational study design, association testing, confounding control, and time-to-event modeling on clinical/EHR phenotypes.
- Computational Toolkit: High proficiency in Python and/or R, version control (Git), and working in cloud-based biobank environments (e.g., DNAnexus, Terra, AWS, or GCP).
- Statistical Methods & Machine Learning: 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); 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.
- Location: Hybrid role, 2 to 3 days onsite at our Menlo Park, CA office.
- Compensation: Annual salary range of $130,000 - $150,000, commensurate with experience. This role is also eligible for equity.
Benefits:
- Comprehensive healthcare coverage (Health, Dental, and Vision)
- 401K
- Unlimited PTO
- Professional development opportunities
- Company-sponsored off-sites and team meals during in-person meetings
- Direct access to company leadership and the opportunity for career growth
MyOme values diversity in all forms. We believe that diverse perspectives drive better science and better patient outcomes. We are an Equal Opportunity Employer committed to creating an inclusive workplace that empowers every individual.
Why Work at MyOme?
Join us if you:
- Want to make an impact at the intersection of healthcare and technology, changing the way people engage with their health at the genetic level
- Enjoy rolling up your sleeves, taking initiative, and being empowered to lead
- Value humility, transparency, and collaborative problem-solving
- Thrive in fast-moving, dynamic environments with smart, driven teammates
- Appreciate competitive compensation, meaningful equity, and excellent benefits
Salary : $130,000 - $150,000