Staff Data Scientist, Genomics

About the role

Biohub is a large-scale initiative aiming to accelerate scientific discovery through the integration of frontier AI models, massive compute, and experimental capabilities. The role involves shaping the future of biological research by pushing the boundaries of AI in science.

Responsibilities

• Set technical vision and strategy for diverse biological data types enabling novel model architectures.
• Define data standards and quality metrics for reliable cross-dataset integration and model-ready data products.
• Develop and validate approaches for combining heterogeneous data modalities into unified training frameworks, focusing on robustness to noise, bias, and batch effects.
• Evaluate representation choices impacting model performance, identifying captured or lost biological signals, and iterating to improve.
• Partner with ML engineers and AI researchers to co-design datasets and feedback loops optimizing model training, evaluation, and generalization.
• Lead cross-functional initiatives spanning data engineering, infrastructure, science, and product, aligning technical execution with long-term scientific goals.
• Identify and drive new data acquisition and generation opportunities, from external collaborations to internal experimental pipelines.
• Serve as a technical mentor and leader, raising the bar for data science and ML rigor across the organization.

Requirements

• PhD in computational biology, bioinformatics, or a quantitative biological field.
• 8+ years of experience working with large-scale biological datasets (genomics, epigenomics, transcriptomics, proteomics, or multi-omics).
• Deep understanding of biological measurement types (sequencing, imaging, proteomics, or related), their underlying data characteristics, and how to transform raw data into AI-ready datasets.
• Experience designing data representations or feature engineering for machine learning in biomedical contexts.
• Strong computational skills (Python, scientific computing libraries) and demonstrated ability to design robust, extensible data architectures and evolve standards in fast-moving scientific domains.
• Strong expertise in machine learning and statistical modeling, with experience applying these methods to data quality assessment, automation, or decision-making systems. Familiarity with modern ML architectures (transformers, diffusion models, or similar) and how data representation choices affect learning.
• Excellent communication skills, with the ability to translate between biology, ML, and engineering audiences.
• Creativity, curiosity, scientific judgment, and a willingness to engage deeply with new biological domains and emerging AI paradigms.

Qualifications

• PhD in computational biology, bioinformatics, or a quantitative biological field.
• 8+ years of experience working with large-scale biological datasets (genomics, epigenomics, transcriptomics, proteomics, or multi-omics).
• Deep understanding of biological measurement types (sequencing, imaging, proteomics, or related), their underlying data characteristics, and how to transform raw data into AI-ready datasets.
• Experience designing data representations or feature engineering for machine learning in biomedical contexts.
• Strong computational skills (Python, scientific computing libraries) and demonstrated ability to design robust, extensible data architectures and evolve standards in fast-moving scientific domains.
• Strong expertise in machine learning and statistical modeling, with experience applying these methods to data quality assessment, automation, or decision-making systems. Familiarity with modern ML architectures (transformers, diffusion models, or similar) and how data representation choices affect learning.
• Excellent communication skills, with the ability to translate between biology, ML, and engineering audiences.
• Creativity, curiosity, scientific judgment, and a willingness to engage deeply with new biological domains and emerging AI paradigms.

Skills

• PhD in computational biology, bioinformatics, or a quantitative biological field.
• Experience with large-scale biological datasets (genomics, epigenomics, transcriptomics, proteomics, or multi-omics).
• Understanding of biological measurement types (sequencing, imaging, proteomics, or related).
• Experience in designing data representations or feature engineering for machine learning in biomedical contexts.
• Strong computational skills (Python, scientific computing libraries).
• Expertise in machine learning and statistical modeling.
• Familiarity with modern ML architectures (transformers, diffusion models, or similar).
• Excellent communication skills.
• Creativity, curiosity, scientific judgment, and willingness to engage deeply with new biological domains and emerging AI paradigms.

Benefits

• Employer match on employee 401(k) contributions.
• Paid time off to volunteer at an organization of your choice.
• Funding for select family-forming benefits.
• Relocation support for employees who need assistance moving.

Pay

The Redwood City, CA base pay range for a new hire in this role is $214,000 - $294,800. New hires are typically hired into the lower portion of the range, enabling employee growth in the range over time. Actual placement in range is based on job-related skills and experience, as evaluated throughout the interview process.

Schedule

This position is a hybrid role requiring you to be onsite for at least 60% of the working month, approximately 3 days a week, with specific in-office days determined by the team’s manager. The exact schedule will be at the hiring manager's discretion and communicated during the interview process.