critical infrastructure that supports the digital age and shapes the spaces where people work, connect, and thrive. From high-performance data centers driving the future of AI to dynamic commercial environments, your wor…
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.
257
Open roles today
$41–$104/hr
Typical pay range
$140k
Median, full-time
11
Fresh in this list
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01
Open data scientist roles
12 shown of 257 · sorted by freshness
$105k - $135k
client-facing web development. The developer will design and maintain SSRS reports and Power BI dashboards that surface trust accounting data to internal and external stakeholders, while also contributing to web-based ap…
Job-ID28987312Reference26-21543Remote50% Remote This position needs to be a strong SAP benefits and payroll functional consultant with at least 8 years of recent SAP configuration experience in the benefits module, 3 of…
benefit plan selections based on represented employee organization changes, and other various reports - mostly centered around payroll data and including FI postings. There is additional ABAP work needed for payroll repo…
leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global de…
$40k - $50k
Sr. Associate - Data Engineering - LATAM About OVG At OVG we help Finance organizations create value with our unique Agentic Performance Management (APM) approach. We are in the early innings of the AI revolution. Our go…
managers to lead the planning, design, documentation, and construction administration of multiple building types, from high-performance data centers that power cloud computing and the digital infrastructure the world rel…
managers to lead the planning, design, documentation, and construction administration of multiple building types, from high-performance data centers that power cloud computing and the digital infrastructure the world rel…
DescriptionAt HDR, our employee-owners are fully engaged in creating a welcoming environment where each of us is valued and respected, a place where everyone is empowered to bring their authentic selves and novel ideas t…
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…
details: # Updated Resume # Current Location # Work Authorization # LinkedIn Id Job Title: Senior Data Engineer/SQL DBA/SSIS Developer Location: Sacramento, CA (Onsite) Industry Experience: Public-sector experience requi…
us to bring our best selves to deliver excellence in support of Solidigm's vision and mission to be the go-to partner for optimized data storage solutions. You can be part of the takeoff of an innovative business that de…
02
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
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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