$131.3k - $237.35k
ability to pass a polygraph examination. Bachelors degree in Data Science, Computer Science, Geospatial Science, or a related... ...efficiency, and mission outcomes. Mentor and train junior data scientists and analysts i…
Engineering & IT · Tucson, AZ
Data Scientist jobs in Tucson, 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.
172
Open roles today
$36–$90/hr
Typical pay range
$122k
Median, full-time
12
Fresh in this list
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01
Open data scientist roles
12 shown of 172 · sorted by freshness
TechniciansProvide mentoring and oversight for less-experienced staff on projects including Federal, Laboratory, Higher Education, and Data Center & Semiconductor.Perform other duties as neededPreferred QualificationsA m…
$68.9k - $131.1k
deter aggression, creating a safer, more secure world. Join us and help shape the future of aerospace and defense.The Senior Analyst in Data Management identifies all data deliverables made part of a contract and subsequ…
to streamline manual processes and improve transparency for tool owners and engineering teams.Build advanced Power BI dashboards and data models leveraging big‑data sets from license logs, license managers, and Splunk re…
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…
for progress because our multidisciplinary teams also include scientists, economists, builders, analysts and artists. That's why we believe... ...where people work, connect, and thrive. From high-performance data centers…
semiconductor company that designs, manufactures and sells analog and embedded processing chips for markets such as industrial, automotive, data center, personal electronics and communications equipment. At our core, we…
$68.9k - $131.1k
participate in design peer reviews, resolving action items, and conduct trade studies to make sound technical decisions.Support technical data package updates and/or variances.Working knowledge of First Article Inspectio…
is generated, transmitted and delivered as global energy demands grow. From massive data centers to modernizing transmission systems, our industry-recognized engineers and scientists have been at the forefront of grid tr…
and making recommendations for mitigation as needed throughout the lifecycle of trenchless projects (planning through construction). Data management, quality control of design and construction data, planning, and risk ma…
DescriptionAbout HDRHDR has maintained a strong presence in Hawai‘i, Guam, and across the Indo-Pacific region for over 40 years. Today,we are recognized as one of the region’s leading providers of integrated architecture…
$68.9k - $131.1k
Job ID: 01871419Posted: Posted TodayStart Date: 2026-09-28Location: US-AZ-TUCSON-M02 ~ 1151 E Hermans Rd ~ BLDG M02Country: United States of AmericaTime Type: Full timeDate Posted:2026-09-28 Country:United States of Amer…
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What data scientists earn in Tucson
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $36–$50 | $75k–$103k |
| Mid level | $50–$68 | $103k–$141k |
| Senior | $65–$90 | $136k–$188k |
Adjusted for the Tucson 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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