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Data Science Startup Jobs in California (NOW HIRING)

Required : • Bachelor's degree in Statistics, Mathematics, Computer Science, Engineering ... Atoms is a robotics startup that develops industrial robotics and physical AI systems to automate ...

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About the team The GTM Data Science team at Airwallex is a collaborative group of analytics and ... Experience in a high-growth startup and/or B2B business models, including CRM, sales pipeline, or ...

You will work on a diverse set of problems, touching every aspect of the startup: extracting ... Contribute to specific data science projects and initiatives at Semgrep; discovering each ...

Data Analyst

San Francisco, CA · On-site

$123K - $160K/yr

Support data scientists by preparing datasets, performing exploratory analysis, and providing ... Prior experience in a startup environment and a desire to make a significant impact.

Company Description Swish Analytics is a sports analytics, betting and fantasy startup building the ... Work closely with Data Scientists and Engineers to diagnose and treat data pipeline integrity ...

Tennis Data Scientist

San Francisco, CA · On-site +1

$135K - $190K/yr

Company Description Swish Analytics is a sports analytics, betting and fantasy startup building the ... Data Science is at the core of our business, so this team has true ownership and impact over ...

Senior Data Scientist

San Francisco, CA · On-site

$175K - $200K/yr

Advanced degree in data science, statistics, computer science, or related field. * Proven expertise ... Experience building classification and prediction models, testing them in a startup environment and ...

Data Scientist

San Francisco, CA · On-site +1

$160K - $200K/yr

Company Description Federato Technologies is a Series A startup based in San Fransisco, CA looking to hire a data scientist. Federato is a venture backed company funded by some of the most prominent ...

Data Scientist

San Francisco, CA · Remote

$160K - $200K/yr

Company Description Federato Technologies is a Series A startup based in San Fransisco, CA looking to hire a data scientist. Federato is a venture backed company funded by some of the most prominent ...

Lead Data Scientist

San Francisco, CA · On-site

$185K - $215K/yr

Advanced degree in data science, statistics, computer science, or related field. * Proven expertise ... Experience building classification and prediction models, testing them in a startup environment and ...

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Data Science Startup information

What is a Data Science Startup job?

A Data Science Startup job involves working at an early-stage company that leverages data science to build innovative products or services. Employees typically wear multiple hats, working on data analysis, machine learning models, data pipelines, and sometimes even product or business strategy. The role often requires strong programming skills (Python, SQL), statistical knowledge, and the ability to work in a fast-paced, evolving environment. Since startups have limited resources, adaptability, problem-solving skills, and a willingness to experiment are crucial. It’s a great opportunity to gain diverse experience and make a significant impact in shaping a company's data strategy.

What are the typical challenges faced when working at a data science startup?

Working at a data science startup often involves managing ambiguity, adapting to frequently changing priorities, and balancing multiple roles due to lean team structures. You may be responsible for the entire data pipeline—from data collection and cleaning to building models and presenting findings—making flexibility and resourcefulness essential. Tight project timelines, evolving business goals, and limited initial resources can also be common, so being proactive and self-motivated helps you thrive. On the positive side, these challenges offer significant opportunities to learn rapidly, take ownership of impactful projects, and shape both the company's products and your own career trajectory.

What are the key skills and qualifications needed to thrive in the Data Science Startup position, and why are they important?

To thrive in a Data Science Startup, you need strong analytical skills, proficiency in programming languages like Python or R, and a solid understanding of statistics and machine learning. Familiarity with data visualization tools, cloud platforms, and relevant certifications such as Certified Data Scientist or AWS Certified Data Analytics are highly valued. Adaptability, creative problem-solving, and effective communication are key soft skills that set standout professionals apart in this environment. These skills are vital for driving innovation, efficiently handling rapidly changing projects, and collaborating in a fast-paced, entrepreneurial setting.

What are the most commonly searched types of Data Science Startup jobs in California? The most popular types of Data Science Startup jobs in California are:
What are popular job titles related to Data Science Startup jobs in California? For Data Science Startup jobs in California, the most frequently searched job titles are:
What job categories do people searching Data Science Startup jobs in California look for? The top searched job categories for Data Science Startup jobs in California are:
What cities in California are hiring for Data Science Startup jobs? Cities in California with the most Data Science Startup job openings:
Infographic showing various Data Science Startup job openings in California as of June 2026, with employment types broken down into 74% Full Time, 24% Part Time, and 2% Temporary. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.
Data Science, Model Development (AI x HealthTech)

Data Science, Model Development (AI x HealthTech)

kadence

Fremont, CA • On-site

$140K - $180K/yr

Other

Posted yesterday


Job description

Data Scientist, Model Development


About the Company

Kadence is partnered with an AI native Healthtech company in the Bay Area, that is building the infrastructure behind modern healthcare operations, turning fragmented healthcare data into reliable, operational systems that power automation, analytics, billing, and customer deployments at scale.


Medical coding is unforgiving: a model error isn't just a bad prediction, it can become a billing or compliance issue. We need someone who can systematically raise model quality through rigorous data science, evaluation, prompt engineering, and domain learning.


About the Role

We're hiring a Data Scientist, Model Development for the company, to build and improve specialty-specific medical coding agents, working closely with engineering, clinical coding experts, auditors, and customers to turn model failures into measurable improvements.


This role blends applied data science, LLM agent development, evaluation design, and healthcare data work - building the feedback loop that turns audited decisions, clinical edge cases, and production failures into stronger agents. We need someone who reasons carefully about data quality, evaluation design, and real deployment risk, not just someone who can train a model.


What You'll Do

  • Model development: Build and iterate specialty-specific coding agents; turn expert feedback into prompt, evaluation, and logic improvements; run structured improvement cycles with regression checks; define readiness criteria for customer-facing models.
  • Evaluation & quality systems: Design gold-standard datasets and evaluation harnesses; build regression testing so changes don't silently break working behavior; develop metrics meaningful to engineers and clinicians; build confidence/uncertainty scoring.
  • Data & pipelines: Curate datasets for training, eval, and customer delivery; support data lineage and auditability; partner with engineering on schemas; support customer pilots and audits.
  • LLM & agent development: Design and iterate prompts for clinical evidence extraction and coding recommendations; build agent scaffolding (prompt chains, eval loops); work toward model-agnostic evaluation across foundation models.
  • Clinical collaboration: Work directly with certified medical coders to understand coding rules and edge cases; translate clinical feedback into model-development tasks; help non-technical stakeholders understand model behavior and tradeoffs.


What We're Looking For

Required

  • 2+ years in data science, applied ML, ML engineering, or LLM-based product development
  • Strong Python; experience with messy, real-world datasets
  • Experience building evaluation frameworks, benchmarks, or regression/quality systems
  • Solid statistical reasoning (precision/recall, confidence intervals, error analysis)
  • Experience with LLMs, prompt engineering, or agentic systems
  • Ability to debug model failures through deep example review and structured iteration
  • Strong communication with non-technical domain experts
  • Comfort with ambiguity in an early-stage startup environment

Preferred

  • Healthcare, revenue cycle, medical coding, claims, EHR, or clinical NLP experience
  • Familiarity with CPT, ICD-10, payer rules, NCCI edits, or CMS/Medicare rules
  • Human-in-the-loop ML, labeling workflows, or expert feedback loops
  • Document AI, OCR, PDF parsing, or clinical note processing
  • Experience with Claude, OpenAI, or other frontier LLM APIs
  • Data versioning, experiment tracking, or ML observability
  • Familiarity with HIPAA, SOC 2, or PHI handling

What Success Looks Like

  • 30 days: Understand current agents, audit workflows, datasets, and major failure modes.
  • 60 days: Own model improvement workstreams; build structured evaluation loops; translate auditor feedback into measurable gains.
  • 90 days: Help establish a repeatable model development system- curated datasets, regression checks, error analysis, confidence metrics.


Location

Bay Area, CA - local candidates strongly preferred.


Compensation

$140,000 - $180,000 base, before equity.