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 · Phoenix, AZ
Data Scientist jobs in Phoenix, AZ
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
839
Open roles today
$39–$98/hr
Typical pay range
$133k
Median, full-time
8
Fresh in this list
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01
Open data scientist roles
12 shown of 839 · sorted by freshness
would like to apply for this position, please contact me as soon as possible. JOB INFORMATION Job Title of Opening: Data Scientist Position is for a Data Scientist (internal title: Technical Project Manager) responsible…
$200k - $250k
Rinvio is hiring a Commissioning Engineer with data center and/or oil and gas experience for high-value commissioning work across the U.S. Pay: $200,000-$250,000/year, based on experience Location: Phoenix, AZ Schedule:…
$144.25k - $256.25k
and efficiency, and drive competitive differentiation with speed. We support the delivery and operations of technology, digital, and data capabilities, platforms, and services globally. Specifically, our team is responsi…
$103.75k - $174.75k
foundation for everything we do in the company while driving differentiation through building and leveraging innovative technology and data insights.At American Express, our mission is to deliver the world’s best custome…
$144.25k - $256.25k
-on experience delivering agentic or LLM-powered systems to productionStrong engineering fundamentals across backend systems, APIs, data pipelines, and cloud infrastructure.Deep experience across the agentic AI stack, in…
$122k - $145k
acceptance criteria* Create process maps that demonstrate a client’s business workflows to assist in stakeholder alignment* Capture data, reporting, security, and user experience needs at a functional level* Validate req…
$123k - $215.25k
core builder responsible for turning complex, ambiguous problems into production-grade agentic systems that operate on real financial data, serve real customers, and meet real regulatory requirements.You will work end to…
$40 per hour
job summary: We are seeking an experienced Databricks Data Engineer to join our high-performing Data & AI practice in Phoenix, AZ. In this role, you will be responsible for designing and deploying enterprise-grade Lakeho…
$86.72k - $119.24k
Position Summary Corning is developing the future of solar module manufacturing in Tolleson, AZ, and we're looking for a Manufacturing Data Engineer to build the data, reporting, and analytics foundation from the ground…
Data Engineer All IT Solutions United States · Phoenix, Arizona Workplace Type — Remote Employment Type — Contract We are currently seeking a qualified Data Engineer to support this engagement. Please review the complete…
We are seeking an experienced Cloud Data Security Assessment Consultant with strong expertise in Google Cloud Platform (GCP) security, data protection, application security, and security architecture assessments. The rol…
02
What data scientists earn in Phoenix
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $39–$54 | $82k–$112k |
| Mid level | $54–$74 | $112k–$153k |
| Senior | $71–$98 | $148k–$204k |
Adjusted for the Phoenix 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
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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
01
Junior Data Scientist
02
Data Scientist
03
Senior Data Scientist
04
Staff Data Scientist
05
Head of Data Science
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