advertisers, and media buyers. Powered by premium video content, robust data, and advanced technology, we’re making it easier for buyers and... ....Job SummaryFreewheel is currently looking to recruit a Data Scientist to…
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.
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Open data scientist roles
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in identity security, purpose-built to answer the fundamental question enterprises face: who can and should take what action on what data. Veza's Access Graph platform maps an organization's entire identity ecosystem acr…
Job-ID27801708Reference26-07957Remote100% Remote Job Title: Contract Data Engineer (API & Database Focus) Role Summary: We are seeking an experienced and highly skilled Contract Data Engineer. This role is focused on bui…
do the same. No matter where you are in the world, this is your chance to be part of something exceptional.Job title: (Senior) Data Scientist / ConsultantLocation: Remote (USA)About the RoleAt Centric, we help some of th…
We are seeking an experienced Kinaxis Data Migration Engineer with 8–10 years of experience in data migration, supply chain systems, data analysis, and enterprise data integration. The ideal candidate will have strong kn…
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…
meaningful work. Today, ServiceNow is the AI control tower for business reinvention. Our ServiceNow AI platform brings together any AI, any data, and any workflow— helping 85% of the Fortune 500 work smarter, faster, and…
meaningful work. Today, ServiceNow is the AI control tower for business reinvention. Our ServiceNow AI platform brings together any AI, any data, and any workflow— helping 85% of the Fortune 500 work smarter, faster, and…
DescriptionA client with Kforce is seeking a Data Engineer II to join their team in Mountain View, CA.Summary:We are looking for creative problem solvers with a passion for tackling tough customer problems involving data…
StatesSalary: $144402 - $234000/yearType: Full time / Regular/PermanentCompany: WalmartBusiness Segment: Home OfficePosition: Senior Data EngineerJob Location: 1375 Crossman Avenue, Sunnyvale, CA 94089Duties: Designs and…
DescriptionKforce has a client that is seeking a Data Scientist II in Mountain View, CA.Overview:We are looking for creative problem solvers with a passion for tackling tough customer problems involving data to serve as…
Data scientist - Agentic AIThis role has been designed as ‘’Onsite’ with an expectation that you will primarily work from an HPE office.Who We Are:Hewlett Packard Enterprise is the global edge-to-cloud company advancing…
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.
What employers ask for
The skills these listings keep naming
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.
Resume tips that move the needle
For data scientists specifically — generic advice costs you here
Lead every bullet with the business result — revenue, retention, cost — and put the method second.
Specify your stack concretely: Python libraries, SQL dialects, experimentation platforms, and any production ML experience.
Distinguish shipped work from research; 'model serving 2M users' and 'notebook analysis' are different claims.
Keep one or two public projects or publications linkable, tailored to the industry you are targeting.
Name the size and kind of data you worked with — event streams, tabular, text — so teams can map you to their problems.
Where this role goes
Typical progression
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