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Data Science Co Op Jobs in California (NOW HIRING)

Maesa

Design Engineer Co-Op

Los Angeles, CA

$20 - $21/hr

This role is ideal for students pursuing a degree in Mechanical Engineering, Packaging Science ... Review product testing data (e.g. drop tests, vibration tests) and document feedback. * Contribute ...

Maesa

Design Engineer Co-Op

Los Angeles, CA · On-site

$20 - $21/hr

This role is ideal for students pursuing a degree in Mechanical Engineering, Packaging Science ... Review product testing data (e.g. drop tests, vibration tests) and document feedback. * Contribute ...

Whatnot

Manager, Data Science

San Francisco, CA · On-site

$221K - $320K/yr

As a remote co-located team, we're inspired by our values and anchored in hubs across the US, UK ... Role We're looking for a Manager, Data Science to lead a high-impact team working on core ...

Samba

Director Data Science, Measurement

San Francisco, CA · On-site

$200K - $250K/yr

Reporting to our VP of Data Science, Samba is looking for a Director of Data Science to lead our ... Co-own the data quality framework that ensures the robustness and consistency of measurement ...

Rundoo

Partner Growth Manager

Redwood City, CA · On-site

$131K/yr

... and co-op channel and its relationships, the Events & Conferences Specialist owns the conferences and regional events, and Data Science builds the enrichment, data, and attribution that all of it ...

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Data Science Co Op information

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$37K

$121.1K

$193.9K

How much do data science co op jobs pay per year?

As of Aug 7, 2026, the average yearly pay for data science co op in California is $121,131.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,200.00 and $134,200.00 per year, depending on experience, location, and employer.

What kinds of projects or tasks can I expect to work on as a data science co op?

As a Data Science Co Op, you may be involved in a variety of projects such as data cleaning, exploratory data analysis, building predictive models, or generating data visualizations to support business decisions. You’ll often work alongside more experienced data scientists, analysts, and cross-functional teams to collaboratively solve real-world problems using data. This role typically emphasizes hands-on learning and practical application of analytical techniques, offering a great opportunity to develop your technical and communication skills. In addition, you may participate in regular meetings, present findings, and contribute to ongoing research or product development initiatives.

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

To succeed as a Data Science Co Op, you should have a solid understanding of statistics, data analysis, and programming, typically gained through coursework or relevant experience in computer science, mathematics, or related fields. Familiarity with tools such as Python or R, SQL databases, and data visualization libraries is highly valuable, and experience with machine learning platforms or certifications can be advantageous. Effective communication, problem-solving, and a collaborative mindset help you excel in team-oriented, fast-paced environments. These competencies are crucial for analyzing complex datasets, delivering actionable insights, and supporting business decision-making.

What is a data science co op?

A Data Science Co-Op is a temporary, structured work experience program for students or early-career professionals to apply data science skills in a real-world setting. Co-Ops typically last several months and involve tasks such as data analysis, machine learning model development, and visualization. Participants work closely with data teams, gaining hands-on experience with tools like Python, SQL, and cloud platforms. Unlike internships, Co-Op positions may be full-time for a semester and often offer deeper engagement with projects. This experience helps build technical skills, industry knowledge, and professional connections for future career opportunities.

What are the most commonly searched types of Data Science jobs in California? The most popular types of Data Science jobs in California are:
What are popular job titles related to Data Science Co Op jobs in California? For Data Science Co Op jobs in California, the most frequently searched job titles are:
What job categories do people searching Data Science Co Op jobs in California look for? The top searched job categories for Data Science Co Op jobs in California are:
What cities in California are hiring for Data Science Co Op jobs? Cities in California with the most Data Science Co Op job openings:
Data Science Co Op jobs near you
Infographic showing various Data Science Co Op job openings in California as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $121,131 per year, or $58.2 per hour.

Co-Op, ML Scientist for Protein Engineering

Lila Sciences

San Francisco, CA

Other

Re-posted 7 days ago

Job description

Your Impact at LILA

Lila is embarking on a transformative mission to redefine the future of medicine by combining automated large-scale data generation with scientific superintelligence. At Lila, we don't just use AI to analyze biology; we are building the loop where AI and automation co-evolve to solve the hardest problems in medicine.

To this end, the Life Science AI team is developing machine learning systems that can reason over biological data and help design better biomolecules. We are seeking an ML Scientist Co-Op to contribute to protein engineering research, including problems related to generative protein design, antibody engineering, developability, and wet-lab-informed model iteration.

This is an opportunity to work alongside Lila scientists on applied ML research at the interface of AI and biology. You will help explore models, datasets, and workflows that connect computational protein design ideas to real experimental needs, gaining hands-on experience in a fast-moving scientific environment.

What You'll Be Building

  • Contribute to ML research projects focused on protein engineering, antibody design, and related biomolecule design problems.
  • Explore generative and predictive modeling approaches for protein sequence, structure, function, and developability.
  • Work with scientists and ML researchers to translate biological design goals into tractable computational problems.
  • Analyze biological and experimental datasets to identify patterns, evaluate model outputs, and guide design decisions.
  • Prototype workflows that connect model predictions, candidate prioritization, and wet-lab feedback.
  • Communicate results clearly through code, notebooks, written summaries, and presentations to scientific and technical collaborators.

What You'll Need to Succeed

  • Currently enrolled as a PhD student in Computer Science, Machine Learning, Computational Biology, Bioengineering, Biophysics, or a related quantitative field.
  • Research experience in machine learning, computational biology, protein engineering, or a closely related area.
  • Strong programming skills in Python and experience with modern ML frameworks such as PyTorch, JAX, or similar tools.
  • Ability to work with biological sequence, structure, assay, or other scientific datasets.
  • Interest in applying ML methods to real biological design problems in partnership with experimental scientists.
  • Clear communication skills and comfort working in a collaborative, cross-disciplinary research environment.

Bonus Points For

  • Experience with protein language models, structure prediction, generative protein design, diffusion or flow-based models, or antibody design.
  • Familiarity with protein structure, biophysics, developability, affinity maturation, or wet-lab validation concepts.
  • Publications, preprints, open-source work, or research projects in ML for biology, protein engineering, or AI for Science.
  • Experience building active learning, model evaluation, or data analysis workflows for scientific discovery.
  • Comfort collaborating with experimental scientists and translating between ML concepts and biological constraints.

Apply