Job Role: Data Engineer with AWS Glue Job Location: Sacramento, CA (Onsite) Job Duration: Long Term Job Summary: We are seeking a highly experienced Senior Snowflake Data Engineer with 10+ years of experience in designin…
Engineering & IT · Sacramento, CA
Data Scientist jobs in Sacramento, 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.
645
Open roles today
$41–$104/hr
Typical pay range
$140k
Median, full-time
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Open data scientist roles
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Join to apply for the Principal Engineer, Data Platforms role at Speridian Technologies 3 days ago Be among the first 25 applicants Join to apply for the Principal Engineer, Data Platforms role at Speridian Technologies…
DISCRETION OF THE DEPARTMENT AND IS SUBJECT TO CHANGE AS BUSINESS NEEDS ARISE. The Employment Training Panel (ETP) is seeking a Research Data Analyst II (RDA II) in the Planning and Research Unit within the Research and…
detail-oriented technology professional to support California’s emerging Artificial Intelligence (AI) Safety Reporting Program . As a Data Reporting Analyst within the Homeland Security Division’s Policy Branch, you will…
$115k - $140k
Salary: $115,000 - 140,000 per year Requirements: We need 5+ years of experience in business intelligence, analytics, or data engineering, including at least 2 years leading a small team or owning a production reporting…
Health (DOSH aka Cal/OSHA), the Analyst II performs complex analytical and technological studies/assignments related to the Cal/OIS data management system. The incumbent works with all programs in DOSH providing guidance…
Job Description and Duties Under general direction of the Research Data Supervisor II (RD Sup II), the Research Data Analyst II (RDA II) is a member of the Research unit responsible for supporting the lead staff over the…
Information Technology Manager II, Chief, Infrastructure Service Branch, the incumbent serves as the Chief of the Storage Engineering & Data Protection Unit. This unit is comprised of Information Technology (IT) professi…
com or call at (***) ***-****. Direct End Client: California Governor's Office of Emergency Services (Cal OES) Job Title: Data Analyst Duration: 36 Months Location: Hybrid / Remote (Candidates must be within 50 miles of…
Design-Bid-Build and Alternative Conceptual-Bid-Build Designing HVAC and plumbing systems for primarily HyperScale and Colocation Data Center design associated with administrative and support spaces Supporting additional…
customer service-oriented team environment, surrounded by enthusiastic and self-motivated people, then look no further! Join our BI Data Analytics and Reporting team as an Information Technology Associate and help delive…
$120k - $150k
international trips to Mexico, Costa Rica, Belize, and the Dominican Republic. Primary Role We are seeking an experienced Senior Data Platform Engineer to architect and scale our data infrastructure. The ideal candidate…
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What data scientists earn in Sacramento
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $41–$57 | $86k–$119k |
| Mid level | $57–$78 | $119k–$162k |
| Senior | $75–$104 | $157k–$216k |
Adjusted for the Sacramento 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
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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
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Lead every bullet with the business result — revenue, retention, cost — and put the method second.
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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
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Junior Data Scientist
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Data Scientist
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Senior Data Scientist
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Staff Data Scientist
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Head of Data Science
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