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Engineering & IT · Kansas City, MO
Data Scientist jobs in Kansas City, MO
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
482
Open roles today
$36–$90/hr
Typical pay range
$122k
Median, full-time
5
Fresh in this list
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01
Open data scientist roles
5 shown of 482 · sorted by freshness
vulnerabilities across endpoints, servers, network devices, cloud workloads, Webservers and OT/manufacturing assetsAnalyze vulnerability data in the context of exploitability, asset criticality, business impact, and OT s…
$119k - $154k
Automation Response (SOAR / Case Management), Endpoint Detection and Response (EDR), Secure Email Gateways, and Email and Endpoint Data Loss Prevention (DLP) to provide optimal security controls across multiple business…
$119k - $169.4k
Job Description:Building trusted markets — powered by our peopleAt Cboe Global Markets, we inspire our people to solve complex challenges together because what we do matters. We provide the financial infrastructure that…
$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…
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What data scientists earn in Kansas City
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 Kansas 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
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.
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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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