CrowdStrike, Inc. is seeking a Senior Engineer for the Data Services team to elevate our database systems. You will work hands-on with Cassandra, ElasticSearch, Kafka and related tech, building automation around large-sc…
Engineering & IT · San Jose, CA
Data Scientist jobs in San Jose, 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.
1,753
Open roles today
$53–$133/hr
Typical pay range
$179k
Median, full-time
4
Fresh in this list
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01
Open data scientist roles
12 shown of 1,753 · sorted by freshness
$80k - $100k
Machine Learning Data Engineer – Remote Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fant…
$90k - $110k
Big Data Engineer – Remote Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic opportu…
$2,000 per month
Deploying next-generation inference hardware at scale requires more than great chips - it demands world-class physical infrastructure. As a Data Center Engineer at Etched, you will own the end-to-end lifecycle of our dat…
Job Description Job Description Data Engineer III itD is seeking a Data Engineer III to design, build, and optimize scalable... ...performance, scalability, and data quality. Partner with Data Scientists, Product Manager…
Data Scientist with QuickSight Knowledge - Twitch Client Office: Santa Clara, California Work Model: Hybrid Type : Contract Mandatory Skills AWS Bedrock Agent Core QuickSight SQL Python Required Technical Skills...
$91k - $147.2k
individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit. Job Function: Data Analytics & Computational Sciences Job Sub Function: Data Engineering Job Category: Sc…
Job Description: We are looking for early-career Data Analytics / Data Science professionals who are passionate about working with data and deriving meaningful insights. Candidates with internship experience, academic pr…
and have the flexibility, benefits, and support to do their best work. Join us and build for travelers everywhere. Principal Data & AI Engineer, Reporting and Insights Introduction to the Team: Our Technology team partne…
Description Description This position is ideal for senior-level data engineering professionals to join the Product Analytics team as... ...enterprise-scale production systems. Collaborate with data scientists, analysts,…
$65.49 per hour
Job Description Job Description Data Engineer 3 Job Details Data Engineer 3 (Contract) Location: San Jose, CA 95110 (Hybrid) Duration: 12/08/2025 to 12/08/2026 Team: DME Planning Strategy & Consolidation About the Role:…
$105.63 per hour
Job Description Job Description Data Scientist Job Details Data Scientist (Contract) Location: San Jose CA 95110 (Hybrid) Duration: 11/17/2025 to 6/12/2026 Team: Express Growth US About the Role: ~ The Express Growth Dat…
02
What data scientists earn in San Jose
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $53–$73 | $110k–$152k |
| Mid level | $73–$100 | $152k–$207k |
| Senior | $96–$133 | $200k–$276k |
Adjusted for the San Jose 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.
04
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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