I am partnered with a Real Time Intelligence Company, who have built a platform that uses AI to transform publicly available data into real time operational insights. With a huge part of their business growing, they are…
Engineering & IT · San Francisco, CA
Data Scientist jobs in San Francisco, CA
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
1,442
Open roles today
$54–$135/hr
Typical pay range
$182k
Median, full-time
4
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01
Open data scientist roles
12 shown of 1,442 · sorted by freshness
Job Title : Data Engineer (Entry Level) Job Type: Full Time - Permanent Location: San Francisco Bay Area, CA Note: Client is looking for US Citizen or Green Card Holder/Must be from big Tech company or small startup or f…
Onsite) 1. Minimum 10 years of relevant experience 2. Primary Skills: Snowflake AWS PySpark 3. Secondary Skills: BI/ Data Analytics understanding Healthcare domain knowledge Role Descriptions: Primary Skills:...
Data Scientist - San Francisco We are seeking a talented and experienced Data Scientist to join our dynamic team in San Francisco. As a Data Scientist, you will play a pivotal role in harnessing data to drive strategic d…
$90k - $160k
technology and build a more sustainable, more inclusive world. Location San Francisco CA Your Role The GenAI Engineer / Data Scientist is responsible for designing, developing, deploying, and maintaining Generative AI (G…
**We’re Hiring: Senior Data Engineer** We are looking for a highly experienced **Senior Data Engineer** with strong technical expertise, excellent English communication skills, and the ability to collaborate effectively…
$138k - $221k
set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary Data Scientist Overview: Are you passionate about building scalable, high-performance data pla…
$25 per hour
and benefits to the everyday debit card. We are looking for a Data Engineer to pioneer the data team at Point and be responsible... ...disaster recovery procedures ~Collaborate with engineers and data scientists on proje…
$255k - $405k
tightly with research and product to translate model advances into tangible developer productivity. About the Role As a Data Scientist on Codex, you will measure and accelerate product-market fit for AI developer tools.…
$160k - $180k
to meet the unique needs and goals of local communities. Kiddom’s high-quality curriculum is layered with robust teacher and leader data insights to drive the continuous improvement of instructional decisions, school/dis…
At Candid Health, we’re searching for our first BI (Analytics) Engineer to bridge Analytics Engineering, Business Intelligence, and Data Analysis. As a key strategic investment for the company and product, you will be re…
$255k - $405k
works within our Applied Engineering organization identifying and responding to fraudsters on our platform. We are looking for a data scientist with anti fraud & abuse experience to help architect and build our next-gene…
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What data scientists earn in San Francisco
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $54–$74 | $112k–$154k |
| Mid level | $74–$101 | $154k–$210k |
| Senior | $98–$135 | $203k–$280k |
Adjusted for the San Francisco market from national ranges.
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What employers ask for
The skills these listings keep naming
Python (pandas, scikit-learn)SQLStatistics and A/B testingMachine learning fundamentalsData visualizationExperiment designCommunicating with stakeholdersDomain and product sense
04
Interview questions worth rehearsing
With the thing the interviewer is actually listening for
Walk me through a data project that changed a decision.
Structure it as question, approach, finding, action. Projects that ended in a decision — even 'we did not launch' — beat technically impressive analyses that went nowhere.
How would you design an A/B test for a new feature?
Cover the metric, randomization unit, sample size, and duration, plus a pitfall like peeking or interference. Practical rigor is what is being tested.
Your model performs well offline but poorly in production. Why might that be?
Discuss train/serve skew, data leakage, distribution shift, and feedback loops. Listing several plausible causes and how you would check each is the strong answer.
Explain p-values or confidence intervals to a non-technical stakeholder.
Use plain language and a concrete scenario, and resist overstating certainty. They are testing whether your statistics survive translation.
How do you handle missing or messy data?
First ask why it is missing — the mechanism matters more than the imputation method. Then discuss options and how the choice affects conclusions.
When would you not use machine learning for a problem?
When a rule, a query, or a simple heuristic wins on cost and interpretability. Knowing when ML is overkill signals maturity.
How do you decide which metric a team should optimize?
Talk about proxy versus true goals, gameability, and counter-metrics. A story about a metric that backfired is very effective here.
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Resume tips that move the needle
For data scientists specifically — generic advice costs you here
01
Lead every bullet with the business result — revenue, retention, cost — and put the method second.
02
Specify your stack concretely: Python libraries, SQL dialects, experimentation platforms, and any production ML experience.
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Distinguish shipped work from research; 'model serving 2M users' and 'notebook analysis' are different claims.
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Keep one or two public projects or publications linkable, tailored to the industry you are targeting.
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Name the size and kind of data you worked with — event streams, tabular, text — so teams can map you to their problems.
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Where this role goes
Typical progression
01
Junior Data Scientist
02
Data Scientist
03
Senior Data Scientist
04
Staff Data Scientist
05
Head of Data Science
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