Entry-Level Quality Assurance (QA) Analyst W2 Contract Location: United States (Multiple Client Locations / Remote, Hybrid & Onsite opportunities based on client requirements) Employment Type: W2 Contract We're Hiring En…
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.
967
Open roles today
$39–$98/hr
Typical pay range
$133k
Median, full-time
3
Fresh in this list
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01
Open data scientist roles
12 shown of 967 · sorted by freshness
$87.05k - $130.57k
As a Data Engineer, you will have the unique opportunity to design and establish a modern data engineering function within our organization. You will take the lead in building the data infrastructure that enables analyti…
candidate will have strong expertise in Python, machine learning algorithms, data processing, model training, and cloud-based AI services. The candidate will work closely with data scientists, software engineers, and bus…
breakthroughs to fuel movements. To learn more about us, visit collaboration.ai. About the Role You'll build the agentic systems and data pipelines behind NetworkOS's AI capabilities: production agent workflows built on…
Job Title: Senior Data Engineer Location Remote, but interviews or laptop pickup must occur at one of the following locations: San Francisco, Arlington, VA, Denver, CO, Atlanta, GA, Chicago, Boston, NYC, Houston, Miami,…
$120.49k - $192.78k
Job Title Data Scientist - Medical Imaging (Plymouth, MN) We are seeking Data Scientist to join our AI/ML Software development team in developing state-of-the-art medical devices with a focus on medical image processing.…
Job Title: Data Scientist Location: Minneapolis, MN Job Summary: System One is seeking a Data Scientist with 5+ years of hands-on experience to design, build, and deploy production-grade machine learning models. In this…
energizing to you ~ You can't wait to get out of bed in the morning & get on with your day Overview We're looking for a Senior Data Platform Engineer to lead the design, implementation, and evolution of our data platform…
Data Engineer Richfield, MN 55423 (Local - Hybrid 2 Days/Week) 12+ Months Contract Project Description: You will be part of the Myads reporting team responsible for building the measurement, reporting, and insights platf…
$30.07 - $37.59 per hour
Title : Data Management Analyst Location : Minneapolis, MN (Hybrid) Job Type : Contract (12 Months) Compensation : $30.07 - $37.59/hr Industry: Retail --- About the Role We are partnering with a leading Fortune 500 gener…
applied mathematics, statistics, engineering, physics, accounting, finance, economics, econometrics, computer sciences, or business/social and behavioral sciences with a quantitative emphasis. 4+ years of SAS or SQL expe…
$72k - $134k
*Securian Financial Groups internal position title is Data Engineering Sr Analyst. Position Summary: Securian Financial is looking... ...meet changing needs. You’ll collaborate closely with data scientists, machine learn…
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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.
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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
05
Head of Data Science
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