About Vero
Vero is redefining preventive health through proteomics and AI. Backed by Khosla Ventures, we've built the first consumer platform that measures and optimizes organ-specific biological age, empowering people to take action before disease begins.
Our proprietary blood assay decodes thousands of proteins to identify early signals of organ aging and health risk. Then, using machine learning models, we generate targeted recommendations and track changes over time. It's precision health designed for the era of personalization.
Our mission is bold and urgent, to make proactive, data-driven health the norm, not the exception.
The Role
As a Computational Analyst at Vero, you'll support the development of organ-specific aging models by preparing and analyzing large-scale biological datasets. You'll work closely with our computational biologist to integrate, and explore multi-omics data, transforming raw signals into the foundation for health insights.
If you're energized by rigorous science, mission-driven work, and the chance to build from the ground up, we'd love to hear from you.
What You'll Do
- Support the curation, cleaning, and formatting of proteomic, clinical, and molecular datasets
- Help integrate multi-omics data from human cohorts into reproducible pipelines
- Assist in exploratory analyses to identify promising biological signals
- Contribute to model development by preparing inputs and organizing outputs
- Access public repositories and proprietary data to expand our analytical scope
- Generate visualizations, documentation, and clear summaries of findings
- Work cross-functionally with computational biologist, data engineers, and product teams
What you Bring
Education & Experience
- Master's in bioinformatics, biostatistics, computational biology, computer science, or a related field - or a Bachelor's degree with 3+ years of relevant industry or academic experience
Omics & Clinical Data Expertise
- Deep experience working with human cohort data, including molecular multi-omics (e.g., proteomics, transcriptomics) and clinical datasets
- Proven ability to identify and utilize external datasets from public repositories and research consortia to support new investigations
Data Science & Modeling
- Strong programming skills in Python and R; working knowledge of SQL
- Solid foundation in statistics, hypothesis testing, and machine learning, with experience using libraries like scikit-learn
- Familiarity with data quality assessment, governance practices, and best-in-class ETL pipeline development
Visualization & Communication
- Proficiency in visualization tools (Matplotlib, Seaborn, Plotly) and a strong sense for data storytelling and scientific communication
- Experience documenting analytical workflows and sharing findings with technical and non-technical audiences
Collaborative and Technical Workflow
- Comfortable with Git and collaborative development workflows
- Experience with cloud platforms (e.g., Google Cloud) and modern data infrastructure practices
Preferred
- Familiarity with big data tools like PySpark and distributed computing principles
- Knowledge of data warehousing systems such as Google BigQuery
- Awareness of data privacy standards such as HIPAA
- A thoughtful approach to problem-solving and a sensitivity to the ethical dimensions of health data
- Bay Area location preferred