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Biotech Data Science Jobs in Berkeley, CA (NOW HIRING)

PhD in Life Sciences, Computational Biology, Bioinformatics, Biotechnology, or a related discipline ... Experience leveraging AI, machine learning, or data science tools within research environments is ...

PhD in Life Sciences, Computational Biology, Bioinformatics, Biotechnology, or a related discipline ... Experience leveraging AI, machine learning, or data science tools within research environments is ...

PhD in Life Sciences, Computational Biology, Bioinformatics, Biotechnology, or a related discipline ... Experience leveraging AI, machine learning, or data science tools within research environments is ...

PhD in Life Sciences, Computational Biology, Bioinformatics, Biotechnology, or a related discipline ... Experience leveraging AI, machine learning, or data science tools within research environments is ...

Lead execution across science, engineering, lab and greenhouse operations, --coordinating ... software/data and synthesizing ambiguity into executable plans. * Experience developing and ...

Lead execution across science, engineering, lab and greenhouse operations, -coordinating ... software/data and synthesizing ambiguity into executable plans. * Experience developing and ...

Lead execution across science, engineering, lab and greenhouse operations, -coordinating ... software/data and synthesizing ambiguity into executable plans. * Experience developing and ...

Senior Data Engineer

Redwood City, CA · On-site

$129K - $176K/yr

Experience in life sciences, biotechnology, pharmaceutical R&D, clinical development, precision medicine, or another regulated data environment. * Experience with data cataloging, metadata management ...

Showing results 41-60

Biotech Data Science information

See Berkeley, CA salary details

$45.9K

$150.3K

$240.6K

How much do biotech data science jobs pay per year?

As of Aug 22, 2026, the average yearly pay for biotech data science in Berkeley, CA is $150,286.00, according to ZipRecruiter salary data. Most workers in this role earn between $120,600.00 and $166,500.00 per year, depending on experience, location, and employer.

What is a biotech data science?

A Biotech Data Science job involves analyzing complex biological and pharmaceutical data to drive research, innovation, and decision-making. Professionals in this field use machine learning, statistical modeling, and bioinformatics tools to extract insights from genomics, clinical trials, and drug discovery datasets. They collaborate with scientists, engineers, and healthcare professionals to improve treatments, develop new therapies, and optimize bioprocesses. Strong programming skills, domain knowledge in biology or biotechnology, and expertise in data analysis are essential for success in this role.

What are the key skills and qualifications needed to thrive in biotech data science, and why are they important?

To thrive in Biotech Data Science, you need a solid background in biology or biotechnology, strong statistical and analytical skills, and experience with data analysis languages like Python or R. Familiarity with bioinformatics tools, sequencing platforms, and data visualization software is often expected, with certifications in data science or related fields considered a plus. Excellent problem-solving, communication, and collaboration skills are essential when working across multidisciplinary teams. These competencies enable effective interpretation of complex biological data, driving innovation and insights in the biotech industry.

What are the most common challenges faced by professionals in biotech data science roles?

One of the primary challenges in Biotech Data Science is working with large, complex, and sometimes incomplete biological datasets, which require advanced analytical approaches and careful data curation. Professionals often need to stay current with rapidly evolving technologies and methods, which can be demanding but also rewarding for those who enjoy continuous learning. Collaboration with scientists, engineers, and regulatory teams is common, so adapting communication styles and translating technical findings to diverse audiences is key. Overcoming these challenges leads to meaningful scientific discoveries and significant career growth opportunities.

What job categories do people searching Biotech Data Science jobs in Berkeley, CA look for?

The top searched job categories for Biotech Data Science jobs in Berkeley, CA are:

What cities near Berkeley, CA are hiring for Biotech Data Science jobs?

Cities near Berkeley, CA with the most Biotech Data Science job openings:

Infographic showing various Biotech Data Science job openings in Berkeley, CA as of August 2026, with employment types broken down into 55% Full Time, 13% Part Time, 11% Temporary, and 21% Contract. Highlights an 74% In-person, and 26% Remote job distribution, with an average salary of $150,286 per year, or $72.3 per hour.

Director / Senior Director, In Silico Data Operations

Violet Research Institute

Berkeley, CA • On-site, Remote

Full-time

Posted 2 days ago

New


Job description

About Violet Research Institute

Violet Research Institute (VRI) is building the future of personalized medicine for patients with genetic diseases. We're at the frontier of a new era in medicine — one where treatments can be designed for individual patients based on their unique biology. Recent breakthroughs in science, engineering, and regulatory pathways have made this possible, but much of this work remains nascent and distributed across disparate efforts globally. We're unifying, refining, and scaling these efforts into a cohesive platform. For each patient we serve, we deeply understand their biology, then design and manufacture a targeted treatment that can be delivered in months instead of years.

We combine the urgency and execution mindset of a startup with the mission-driven openness of a nonprofit, allowing us to collaborate broadly and move quickly on behalf of the patients we serve. We've brought together leading researchers, engineers, and organizations across omics, therapeutic design, manufacturing, clinical care, and AI to move from insight to action as quickly as possible.

VRI is founded by the family of our first patient, Violet, and is led by Michael Buckley, Siranush Babakhanova and Steve Turner. Our team is deeply cross-disciplinary and first-principles driven. We value builders, experts, and generalists who are excited to work across domains, challenge conventional approaches, and increase access to personalized medicine.

What We're Looking For

Our data infrastructure is growing faster than our ability to govern it. We need someone who thinks in systems, speaks the language of computational biology, and can build the standards, tooling, and team structure that lets our science scale without breaking. This role owns our in silico and data operations: how assay data flows from raw output to structured insight, how our bioinformatics team is organized and directed, and how we evaluate, implement, and scale platforms like Benchling. You are native in AI, not just tool-adjacent, but someone who reaches for it instinctively as part of how you build and think. This role is fully remote and open to candidates anywhere in the United States. You will report to an East Coast–based leader and work closely with our existing bioinformatics team. Over time, as trust is established, this role will take on direct management of data scientists on the team.

What You'll Own
  • LIMS ownership: our current LIMS implementation is struggling, and you will own the path forward — either fixing it or evaluating and leading a replacement (Benchling, Dotmatics, or comparable). This role requires a full-ownership mentality, not a recommendation-from-the-sidelines posture
  • Data infrastructure and standards: repeatable formats for raw assay data, ASO design files, in vitro and in vivo readouts, and CRO deliverables; you direct the bioinformatics team to execute against those standards
  • Decision and program tracking systems: a single source of truth for what is being done, by whom, by when, and what comes next — from a data and decision infrastructure perspective
  • AI-native workflows: build and maintain AI-assisted pipelines for data analysis, literature synthesis, and scientific decision support
  • Bioinformatics team direction: work closely with bioinformaticians (and over time manage data scientists) to ensure computational outputs meet scientific and operational standards
  • CRO data quality: own the incoming data QC process for computational and omics deliverables; escalate and remediate when vendor outputs fail standards
  • Close partnership with the Wet Lab Scientific Operations Director to keep experimental and computational programs in sync, including shared raw-data standards adopted across CROs and internal workflows
Goals for the First 90 Days
  • Audit the current data infrastructure across SYT1 ASO 1, ASO 2 screen, the cocktail program, and siRNA exploratory work; produce a clear, written assessment of gaps and recommendations
  • Define the raw-data standard for assay outputs (knockdown, off-target, tox, PK readouts) and have it adopted across at least two CROs and internal workflows
  • Deliver a written recommendation on the LIMS — fix in place or replace with Benchling (or comparable) — with implementation plan and timeline
  • Stand up a program tracker and decision log that the founder, lead scientist, and senior advisors actually use
  • Establish a working relationship with the bioinformatics team and identify any gaps in tooling, documentation, or workflow standards
Minimum Qualifications
  • PhD in computational biology, bioinformatics, biostatistics, computer science, or a closely related field
  • 7+ years in computational biology, bioinformatics, or data science within a drug discovery or biotech context
  • Hands-on experience building and owning data infrastructure in a small, resource-constrained organization — not just contributing to it
  • Comfort with nucleic acid sequence design and navigating sequence design space (ASO, siRNA, or related modalities)
  • Fluency with cloud data platforms (GCP, AWS) and bioinformatics workflow tools
  • Native AI user — you build with AI, not around it; tools like Claude, Copilot, or equivalent are a daily part of how you work
  • Experience evaluating or deploying scientific data management platforms (Benchling, Dotmatics, or comparable)
  • Track record of building operational systems and standards from scratch
  • Comfort directing senior PhD scientists without being one — credibility comes from clarity and precision, not titles
Preferred Qualifications
  • Prior experience integrating wet lab and dry lab experiments into a coherent pipeline — from assay design through data structure to interpretation
  • Experience with oligonucleotide, siRNA, or gene therapy program data — familiarity with the data types and QC challenges specific to nucleic acid therapeutics
  • Prior management or formal mentorship of data scientists or bioinformaticians
  • Familiarity with regulatory data requirements in a preclinical-to-IND context
  • Experience inside a small biotech, founder-led nonprofit, or intensely mission-driven organization
  • Working knowledge of bDNA / QuantiGene, ddPCR, or qPCR data artifacts in nucleic acid quantification
Behavioral Essentials
  • Execute independently from loosely specified tasks
  • Ask for help only when truly blocked, communicating clearly what's needed
  • Thrive in early-stage, ambiguous, high-pace environments
  • Mission-driven with genuine care for patient impact