$71.3k - $140.6k
clients improve financial performance, accelerate new digital ventures, and fuel growth through innovation. Work you'll do As a PROJECT - Data Engineer II on the AI & Data team, you will be responsible for… Designing, de…
Engineering & IT · Salt Lake City, UT
Data Scientist jobs in Salt Lake City, UT
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
363
Open roles today
$39–$99/hr
Typical pay range
$134k
Median, full-time
10
Fresh in this list
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Open data scientist roles
12 shown of 363 · sorted by freshness
Divisional Overview:The Risk Division is a team of specialists charged with managing the firm's credit, market, liquidity, operational and insurance risk. Whether assessing the creditworthiness of the firm's counterparti…
Known for being a great place to work and build a career, KPMG provides audit, tax and advisory services for organizations in today's most important industries. Our growth is driven by delivering real results for our cli…
$145k - $181k
What You’ll Do• Architect, build, and optimize cloud platform solutions across AWS, Azure, and/or Google Cloud environments.• Develop Infrastructure as Code (IaC) and automation frameworks using tools such as Terraform,…
$120k - $155k
Location: All Locations in the United States; preference given to candidates near an Eide Bailly location. Work Arrangement: Remote A Day in the Life The Cloud Senior Engineer is responsible for supporting complex engine…
$167k - $203k
Premises), SIP (Session Initiation Protocol) trunks, number porting, sites, and Edge configuration· Build integrations using Genesys Cloud Data Actions, the Platform API (Application Programming Interface), AppFoundry ap…
We are looking for a Data Engineer to help design and enhance data solutions that support reliable reporting and analytics in Salt Lake City, Utah. This role focuses on building scalable data pipelines, shaping well-stru…
$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…
$77k - $202k
context and how it is changing.Use reflection to develop self awareness, enhance strengths and address development areas.Interpret data to inform insights and recommendations.Uphold and reinforce professional and technic…
reasonable estimate of the current range is: Grade: Management_Executive 609Pay Range: $116,600.00 - $209,900.00Job DescriptionAt WGU, data is a powerful tool for understanding student experiences, driving institutional…
$79.6k - $139.3k
Senior Data Engineer (Databricks & Cloud Analytics) Position Description Are you passionate about building modern data platforms... ...will partner with solution architects, business analysts, data scientists, and client…
$72k - $115k
set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary Data Scientist I Who is Mastercard? As a global technology company our mission at Mastercard i…
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What data scientists earn in Salt Lake City
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $39–$54 | $82k–$113k |
| Mid level | $54–$74 | $113k–$154k |
| Senior | $72–$99 | $149k–$206k |
Adjusted for the Salt Lake City 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
01
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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