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Lab Data Analyst Jobs in California (NOW HIRING)

Atoms

Data Annotation Analyst

San Francisco, CA · On-site

$71K - $89K/yr

We don't just build machines in a lab. We deploy them into real environments, operate them, learn ... Upload prioritized image data for labeling * Create and track metrics for labelers * Iterate on ...

Actalent

Harvest Lab Technicians

Napa, CA

$25 - $26/hr

Maintain a clean and organized work environment * Assist with data tracking and analysis scheduling ... lab reports for the winemaking team * Cross-train with other departments and provide coverage as ...

Labric

Full Stack Software Engineer

San Mateo, CA · On-site

$140K - $200K/yr

About Labric Effective use of AI will transform scientific research, but most lab data is stuck in ... Scientists use it for cross-experimental analysis, visibility into colleagues' work, and ...

Labric

Senior Full Stack Software Engineer

San Mateo, CA · On-site

$180K - $240K/yr

About Labric Effective use of AI will transform scientific research, but most lab data is stuck in ... Scientists use it for cross-experimental analysis, visibility into colleagues' work, and ...

QuantumClean

Lab Analyst I

Fremont, CA · On-site

$62K - $74K/yr

UCT is looking for a talented Lab Analyst I to join us in Fremont, CA! This position is responsible ... Inputs data for reports and summaries. * Performs routine maintenance of instruments and sample ...

Pave

Insights Analyst

San Francisco, CA

$139K - $174K/yr

Support Pave's data products, including Pave Data Lab and Market Data, with research, analysis, and special projects that keep our content fresh and differentiated * Evangelize Pave's compensation ...

Earli

Senior Scientist, Bio AI

Redwood City, CA · On-site

$112K - $153K/yr

... lab data and create a rapid iteration loop • Perform routine computational analyses to analyze MPRA and other large experimental datasets using existing packages as well as bespoke analysis where ...

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Lab Data Analyst information

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How much do lab data analyst jobs pay per hour?

As of Aug 16, 2026, the average hourly pay for lab data analyst in California is $27.12, according to ZipRecruiter salary data. Most workers in this role earn between $19.47 and $32.26 per hour, depending on experience, location, and employer.

What does a lab data analyst do?

A Lab Data Analyst is responsible for collecting, processing, and analyzing data generated in a laboratory setting. They ensure data accuracy, interpret results, and generate reports to support research, quality control, or regulatory compliance. Their role often involves working with lab instruments, databases, and statistical software to identify trends and insights. Additionally, they collaborate with scientists, engineers, and other professionals to improve processes and ensure data integrity.

What does a typical workday look like for a lab data analyst?

A typical day for a Lab Data Analyst involves collecting, processing, and analyzing laboratory data to ensure accuracy and compliance with quality standards. You'll collaborate closely with laboratory technicians and scientists to interpret results, generate reports, and provide data-driven insights for ongoing projects or research. Most of your time will be spent working with digital data systems and reviewing results, but you may also be involved in troubleshooting data discrepancies or optimizing data workflows. The role is dynamic and often requires balancing multiple tasks, such as participating in team meetings, documenting findings, and supporting process improvements.

What are the key skills and qualifications needed to thrive as a lab data analyst, and why are they important?

To thrive as a Lab Data Analyst, you need strong analytical skills, attention to detail, and a degree in a relevant field such as biology, chemistry, or data science. Familiarity with laboratory information management systems (LIMS), statistical analysis software, and data visualization tools like Excel or Tableau is typically required, and certifications in data analysis are a plus. Excellent problem-solving abilities, communication skills, and the capacity to work collaboratively with scientists and technicians will help you stand out. These competencies ensure accurate data interpretation, effective reporting, and successful teamwork within laboratory environments.

What are the most commonly searched types of Lab Data Analyst jobs in California?

The most popular types of Lab Data Analyst jobs in California are:

What are popular job titles related to Lab Data Analyst jobs in California?

For Lab Data Analyst jobs in California, the most frequently searched job titles are:

What job categories do people searching Lab Data Analyst jobs in California look for?

The top searched job categories for Lab Data Analyst jobs in California are:

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Lab Data Analyst jobs near you
Infographic showing various Lab Data Analyst job openings in California as of August 2026, with employment types broken down into 85% Full Time, 10% Part Time, and 5% Nights. Highlights an 95% In-person, and 5% Hybrid job distribution, with an average salary of $56,417 per year, or $27.1 per hour.

Data Engineer, Scientific Data Ingestion

Mithrl

San Francisco, CA • On-site

$150K - $200K/yr

Full-time

Medical, Dental, Vision, Retirement

Re-posted 21 days ago

Job description

ABOUT MITHRL
We envision a world where novel drugs and therapies reach patients in months, not years, accelerating breakthroughs that save lives.
Mithrl is building the world's first commercially available AI Co-Scientist-a discovery engine that empowers life science teams to go from messy biological data to novel insights in minutes. Scientists ask questions in natural language, and Mithrl answers with real analysis, novel targets, and patent-ready reports.
Our traction speaks for itself:
  • 12X year-over-year revenue growth
  • Trusted by leading biotechs and big pharma across three continents
  • Driving real breakthroughs from target discovery to patient outcomes.

WHAT YOU WILL DO
Build and own an AI-powered ingestion & normalization pipeline to import data from a wide variety of sources - unprocessed Excel/CSV uploads, lab and instrument exports, as well as processed data from internal pipelines.
Develop robust schema mapping, coercion, and conversion logic (think: units normalization, metadata standardization, variable-name harmonization, vendor-instrument quirks, plate-reader formats, reference-genome or annotation updates, batch-effect correction, etc.).
Use LLM-driven and classical data-engineering tools to structure "semi-structured" or messy tabular data - extracting metadata, inferring column roles/types, cleaning free-text headers, fixing inconsistencies, and preparing final clean datasets.
Ensure all transformations that should only happen once (normalization, coercion, batch-correction) execute during ingestion - so downstream analytics / the AI "Co-Scientist" always works with clean, canonical data.
Build validation, verification, and quality-control layers to catch ambiguous, inconsistent, or corrupt data before it enters the platform.
Collaborate with product teams, data science / bioinformatics colleagues, and infrastructure engineers to define and enforce data standards, and ensure pipeline outputs integrate cleanly into downstream analysis and storage systems.
WHAT YOU BRING
Must-have
  • 5+ years of experience in data engineering / data wrangling with real-world tabular or semi-structured data.
  • Strong fluency in Python, and data processing tools (Pandas, Polars, PyArrow, or similar).
  • Excellent experience dealing with messy Excel / CSV / spreadsheet-style data - inconsistent headers, multiple sheets, mixed formats, free-text fields - and normalizing it into clean structures.
  • Comfort designing and maintaining robust ETL/ELT pipelines, ideally for scientific or lab-derived data.
  • Ability to combine classical data engineering with LLM-powered data normalization / metadata extraction / cleaning.
  • Strong desire and ability to own the ingestion & normalization layer end-to-end - from raw upload → final clean dataset - with an eye for maintainability, reproducibility, and scalability.
  • Good communication skills; able to collaborate across teams (product, bioinformatics, infra) and translate real-world messy data problems into robust engineering solutions.

Nice-to-have
  • Familiarity with scientific data types and "modalities" (e.g. plate-readers, genomics metadata, time-series, batch-info, instrumentation outputs).
  • Experience with workflow orchestration tools (e.g. Nextflow, Prefect, Airflow, Dagster), or building pipeline abstractions.
  • Experience with cloud infrastructure and data storage (AWS S3, data lakes/warehouses, database schemas) to support multi-tenant ingestion.
  • Past exposure to LLM-based data transformation or cleansing agents - building or integrating tools that clean or structure messy data automatically.
  • Any background in computational biology / lab-data / bioinformatics is a bonus - though not required.

WHAT YOU WILL LOVE AT MITHRL
  • Mission-driven impact: you'll be the gatekeeper of data quality - ensuring that all scientific data entering Mithrl becomes clean, consistent, and analysis-ready. You'll have outsized influence over the reliability and trustworthiness of our entire data + AI stack.
  • High ownership & autonomy: this role is yours to shape. You decide how ingestion works, define the standards, build the pipelines. You'll work closely with our product, data science, and infrastructure teams - shaping how data is ingested, stored, and exposed to end users or AI agents.
  • Team: Join a tight-knit, talent-dense team of engineers, scientists, and builders
  • Culture: We value consistency, clarity, and hard work. We solve hard problems through focused daily execution
  • Speed: We ship fast (2x/week) and improve continuously based on real user feedback
  • Location: Beautiful SF office with a high-energy, in-person culture
  • Benefits: Comprehensive PPO health coverage through Anthem (medical, dental, and vision) + 401(k) with top-tier plans

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

Apply