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Engineering & IT · Minneapolis, MN
Data Scientist jobs in Minneapolis, MN
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
850
Open roles today
$39–$98/hr
Typical pay range
$133k
Median, full-time
12
Fresh in this list
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01
Open data scientist roles
12 shown of 850 · sorted by freshness
$98.18k - $115.5k
At U.S. Bank, we’re on a journey to do our best. Helping the customers and businesses we serve to make better and smarter financial decisions and enabling the communities we support to grow and succeed. We believe it tak…
Position Summary We are seeking an analytical and innovative Data Scientist to help drive business transformation through Advanced Analytics, Artificial Intelligence (AI), and Machine Learning (ML). This role focuses on…
$45 - $55 per hour
DescriptionKforce has a client that is seeking a hybrid Senior Data Engineer to join their growing team in Minneapolis, MN. This team is focused on the -ethics, compliance, HR and legal- data aspect of the client and in…
$92.3k - $138.5k
Mortenson is currently seeking a Data Scientist that will be responsible for modeling complex business problems and discovering business insights through the use of statistical, algorithmic, mining, and visualization tec…
Job-ID29238416Reference26-25469Seeking a Principal Microsoft Data & AI Engineer who combines advanced SQL and Microsoft Fabric expertise with hands-on data engineering, data wrangling, analytical modeling, and practical…
$132.26k - $155.6k
career. Try new things, learn new skills and discover what you excel at—all from Day One.Job DescriptionJob Duties -Responsible for big data/ analytics projects that gather and integrate large volumes of data. -Specializ…
Managing Director, Data Engineering & AIWho You'll Work WithAs a Managing Director in Slalom's Data & AI practice, you will lead the growth of our Data Engineering & AI business by helping clients modernize their data fo…
$85.6k - $149.4k
The Clinical Informaticist combines clinical expertise, healthcare informatics, and data science to support the development, implementation, and optimization of Wolters Kluwer Health solutions. This role applies knowledg…
to join a retail organization on a Contract basis in Bloomington, Minnesota. This position focuses on turning marketing and customer data into actionable insights through AI-driven analysis, predictive modeling, and perf…
Data Engineer Location: Minnesota (MN) Job Description: We are seeking an experienced Data Engineer with strong expertise in Python, PySpark, SQL, and Databricks to design, develop, and optimize scalable data pipelines a…
Job-ID29338947Reference26-26568Required Qualifications • Strong SQL and data analysis skills. • Experience working with large-scale data warehouses and analytics platforms. • Experience with GCP data environments, includ…
02
What data scientists earn in Minneapolis
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 Minneapolis 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.
05
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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