$148k - $185k
cultivating a work environment where all team members belong and have the opportunity to thrive. Data Science is at the heart of Lyft's products and decision-making. Data Scientists at Lyft operate in dynamic environment…
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.
1,573
Open roles today
$54–$135/hr
Typical pay range
$182k
Median, full-time
6
Fresh in this list
No email, no resume, no sign-up. Save any listing below and you start anonymously.
You're signed in. Saving a listing drops it straight into your pipeline.
01
Open data scientist roles
12 shown of 1,573 · sorted by freshness
$186k - $230k
technology evolves. AI experience requirements vary by role and will be assessed during the interview process.About the Role:As a Data Scientist supporting Risk, you will play a crucial role in leveraging experimentation…
their family, personal goals, and other priorities. We can hire people in any country where we have a legal entity.Atlassian’s Trusted Data Platform (TDP) provides secure, reliable, and scalable data-storage capabilities…
$177.19k - $364.8k
here.We are looking for an Analytics Engineering Manager II, Core Data Science to help improve the quality and velocity of data... ...verification to ensure correctness and quality.Work closely with Data Scientists and E…
$132.26k - $155.6k
career. Try new things, learn new skills and discover what you excel at—all from Day One.Job DescriptionJob Duties -Responsible for big data/ analytics projects that gather and integrate large volumes of data. -Specializ…
$159.2k - $301.6k
About the roleAdobe’s Security Data Platform team builds and operates a petabyte-scale security data lakehouse that turns enterprise telemetry into trusted data for threat detection, investigations, compliance, and secur…
$293k - $325k
compute infrastructure scales efficiently to support millions of users and increasingly sophisticated AI models.We’re looking for a Data Scientist to partner closely with Capacity Systems Engineering, Infrastructure, Pro…
$206.6k - $229k
future of Salesforce.What’s the opportunity? The Research, Analytics & Data Science (RAD) team at Intercom uses data and insights to drive evidence-based decision-making. We're a team of data scientists and product resea…
$190.8k - $262.8k
is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam. Senior Data Scientist - Network Value (Credit) The Network Value Data Science team is helping Plaid bu…
$190k - $220k
fluency as the technology evolves. AI experience requirements vary by role and will be assessed during the interview process. The Data Engineering team builds tools and systems that make Gusto's data consistent, user-fri…
$150k - $200k
Our client, a growing AI and Data organization, is hiring a Founding Data Engineer to join the team. The successful candidate will help transform enterprise data into actionable intelligence by building the backend syste…
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…
02
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.
03
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.
05
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.
03
Distinguish shipped work from research; 'model serving 2M users' and 'notebook analysis' are different claims.
04
Keep one or two public projects or publications linkable, tailored to the industry you are targeting.
05
Name the size and kind of data you worked with — event streams, tabular, text — so teams can map you to their problems.
06
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
Ten quiet minutes a day
Applying for data scientist jobs in San Francisco?
Robbi carries this page into your first day: your role, your city, your shift preference. Then it hands you a few small things each morning and keeps the pipeline honest.
Save what looks right here, then let Robbi hand you a few small things each morning and keep the follow-ups honest.
Start with this search
No email, no resume, no sign-up.
Open your tracker
Everything you saved is already there.