Senior Staff Data Scientist, Virtual Biology Initiative – AI Research

Biohub

Senior Data Scientist at Biohub, working on biological data representation and AI integration. Leading initiatives in data science for AI-powered biological research.

Posted 5/13/2026full-timeNew York City • California, New York • 🇺🇸 United StatesSenior💰 $241,000 - $331,100 per yearWebsite

About the role

Key responsibilities & impact

Set technical vision and strategy for the design of data representations and tokenization strategies across biological data types—including imaging, sequencing, and multimodal data—that enable novel model architectures
Develop, deploy and validate approaches for combining heterogeneous data modalities into unified training frameworks, designing for robustness to noise, bias, and batch effects
Evaluate model performance, identifying which biological signals are captured or lost and iterating to improve
Partner deeply with ML engineers and AI researchers to co-design datasets and optimize 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 consortium partnerships to internal experimental pipelines
Serve as a technical mentor and leader, raising the bar for data science and ML rigor across the organization

Requirements

What you’ll need

12+ years of experience (or PhD + 7 years) working with large-scale biological datasets, including ownership of end-to-end data products
Deep expertise in at least one of: (a) imaging data—microscopy, cell phenotyping, spatial biology, and the data characteristics of image-based biological measurement; or (b) genomics data—bulk and single-cell sequencing, functional genomics, epigenomics, transcriptomics, spatial biology, and/or multi-omics
Understanding of how to transform raw biological data into AI-ready datasets, including familiarity with scientific best practices, noise characteristics, batch effects, and quality assessment specific to your domain
Experience with tokenization strategies for non-text data (images, sequences, graphs, time series) or with creating data representations and feature engineering for machine learning in scientific or biological contexts
Strong expertise in data science and statistical modeling; familiarity with modern ML architectures (transformers, diffusion models, or similar) and how data representation choices affect learning
Strong computational skills; demonstrated ability to design robust, extensible data architectures
Excellent communication and leadership skills, with the ability to translate between biology, ML, and engineering audiences and align teams to deliver complex projects
Creative, first-principles thinking about how to structure data for learning

Benefits

Comp & perks

Provides a generous employer match on employee 401(k) contributions to support planning for the future.
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

ATS Keywords

✓ Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data representationtokenization strategiesbiological data typesimaging datagenomics datadata engineeringstatistical modelingmachine learning architecturesfeature engineeringdata architectures

Soft Skills

communication skillsleadership skillsmentorshipcollaborationcreative thinkingproblem-solvingcross-functional leadershipalignment of teamstechnical visionstrategic thinking