$135k - $150k
Resource Innovations is seeking a Lead Java Software and Data Integration Engineer to join our growing Software as a Service (SaaS) team. As a hands‑on technical lead at Resource Innovations, you will be instrumental in…
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.
1,476
Open roles today
$39–$98/hr
Typical pay range
$133k
Median, full-time
2
Fresh in this list
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01
Open data scientist roles
11 shown of 1,476 · sorted by freshness
$55 - $65 per hour
Title: Data Engineer Location: Tempe, AZ (Onsite) Contract: 12 month Schedule: Monday-Friday | 8:00 AM-5:00 PM Benefits: This position is eligible for medical, dental, vision, and 401(k) . Pay: 55$ - 65$ an hour Position…
Role Description ~12+ years of experience as a Data Engineer. ~ Design, develop, and maintain scalable, resilient data engineering solutions. ~ Strong expertise in Snowflake, Python, PySpark, DBT, Qlik Replicate, and Air…
Kforce has a client in Phoenix, AZ who is seeking a Data Engineer to join their team. The Data Engineer is a hands-on technical role focused on designing, developing, and optimizing enterprise data solutions. This indivi…
$123k - $163k
00 per year Requirements: We require a bachelors degree in Data Science, Statistics, Computer Science, Mathematics, Engineering... ...More: We are Kforces client in Phoenix, AZ, seeking a Data Scientist II to support adv…
Kforce's client in Phoenix, AZ is seeking a Data Scientist II to support advanced analytics and machine learning initiatives that drive business outcomes. This role will focus on analyzing large datasets, developing pred…
$65 - $75 per hour
Apply now: Senior Data Scientist , Remote. Start date is ASAP for this 12 Month Contract position. Job Title: Senior Data Scientist Location/Type: Remote (Candidate must reside in Pacific, Mountain, or Central time zone.…
$50 - $60 per hour
Job Description Job Description Title: Lead Data & AI Engineer Location: Phoenix, AZ (hybrid remote) Type: 6-month contract to hire Pay: $50-60/hr We’re looking for a Lead Data & AI Engineer to lead the design and delive…
working in the office. At that time the expectation is that they will work 100% in the office. Our direct client has an opening for a Data Developer rec 90869. This position is up to 12 months contract to start in Phoeni…
relentless drive to succeed, a strong focus on quality with a passion for success – join us today! UCT is looking for a talented Data Scientist to join us in Chandler, AZ! Job Summary: The Data Scientist will serve as a…
Job Description Job Description Infomatics is partnered with a large retailer that is hiring a Principal AI Data Scientist on a direct hire/FTE basis near Phoenix, AZ. Can work remote. All applicants must be eligible & w…
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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
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.
03
Distinguish shipped work from research; 'model serving 2M users' and 'notebook analysis' are different claims.
04
Keep one or two public projects or publications linkable, tailored to the industry you are targeting.
05
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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