Lead Data Scientist, Computational Biology & Imaging

Countable Labs•Palo Alto, CA

47d

About The Position

At Countable Labs (formerly Enumerix), we’re reimagining the future of genomics—and we’d love for you to be a part of it! As the innovators behind our groundbreaking Countable PCR platform, we’re building tools that make a real impact in precision medicine. We’re a fast-growing startup fueled by innovation, collaboration, and a mission-driven spirit. If you’re ready to roll up your sleeves, build something from the ground up, and help shape the future of genomics, we want you on our team! Role overview We’re seeking a Lead Data Scientist, Imaging & Computational Biology to serve as a player-coach on our data team. In this role, you’ll be both a hands-on technical contributor and a day-to-day team lead guiding a high-performing group of data scientists and engineers. You’ll apply your expertise in image analysis, signal and data processing, statistical modeling, and machine learning to enhance data quality, interpretability, and scientific impact. At the same time, you will help shape team culture, analytical direction, and technical excellence across the organization. This is a high-impact role at the intersection of data science, software engineering, and genomics, shaping the analytical foundation of Countable’s next-generation platform.

Requirements

PhD (or equivalent experience) in Computational Biology, Bioinformatics, Computer Science, Applied Physics, or a related quantitative field.
5+ years of industry experience building imaging or computational analysis tools
Experience managing and leading technical teams, including mentoring scientists/engineers and guiding team execution.
Demonstrated ability to guide complex analytical initiatives or project teams.
Proficiency in Python and MATLAB; experience with C++ or C# is a plus.
Proven track record of delivering robust, production-grade analytical software, including testing, documentation, and performance optimization.
Demonstrated ability to evaluate, structure, and interpret complex, noisy datasets with strong analytical judgment and scientific intuition.
Excellent communication, scientific intuition, and cross-functional collaboration skills.
Thrives in a fast-paced, interdisciplinary startup environment.
Excellent organizational and interpersonal skills, with the ability to motivate, coach, and influence both direct reports and peers.

Nice To Haves

Experience applying deep learning frameworks (PyTorch, TensorFlow) to imaging or biological data.
Strong familiarity with fluorescence imaging, optical microscopy, or advanced quantitative imaging modalities.
Background in spatial or single-cell genomics and high-dimensional biological analysis.
Experience building scientist-facing visualization or analytic tools, especially frameworks used to explore complex datasets.
Demonstrated thought leadership or research experience in computational imaging or ML for biology.

Responsibilities

Lead and mentor a small team while staying deeply hands-on (>50% IC contribution).
Drive sprint planning, guide priorities, run standups, and support day-to-day execution.
Elevate technical rigor, clarity, and analytical thinking through mentorship and review.
Foster a culture of curiosity, accountability, transparency, and collaborative problem solving.
Design algorithms for image processing, dye decoding, noise reduction, feature extraction, and multilabel classification.
Build analysis tools in MATLAB and Python for large, complex biological imaging datasets.
Develop interpretable QC metrics and visualization tools that help scientists assess data quality and reproducibility.
Prototype and validate new approaches using real and synthetic datasets.
Partner with scientists to analyze experiments and diagnose complex data issues.
Identify root causes and propose robust algorithmic or workflow solutions.
Provide analytical insights that inform assay development, validation, and platform direction.
Partner with Product on customer-facing data analysis and reporting features.
Collaborate with engineering to integrate algorithms into production systems.
Communicate analytical decisions and scientific findings clearly across teams.
Establish standards for software quality, testing, documentation, and reproducibility.
Champion best practices in data integrity, statistical rigor, and scientific reasoning.
Stay current with state-of-the-art algorithms in imaging, computational biology, and machine learning.
Guide the evaluation and adoption of new analytical methods that bring scientific or product value.