of a global team of over 50,000 planners, designers, engineers, scientists, digital innovators, program and construction managers and... ...Structural Engineer with experience in hyperscale and colocation data center fac…
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.
770
Open roles today
$36–$90/hr
Typical pay range
$122k
Median, full-time
1
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01
Open data scientist roles
5 shown of 770 · sorted by freshness
About The Role We are looking for a Data Scientist to join our growing analytics team. You will be analyzing large datasets to extract meaningful insights and build machine learning models. Responsibilities Analyze compl…
Our team brings expertise across artificial intelligence and data, digital experience, and platform engineering, enabling the organizations... ...time zones. About the Role We're looking for a Data Scientist to support a…
collaboration and an unwavering dedication to our mission and core values. Are you ready to do work that matters? Summary The Data Scientist I - Applied AI position contributes to the end-to-end design, development, oper…
$50 - $60 per hour
commercial clients, offering innovative solutions to combat indoor and outdoor air pollution. We are seeking a skilled and motivated Data Engineer to join our team and build the data infrastructure that powers our enviro…
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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.
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
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Data Scientist
03
Senior Data Scientist
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Staff Data Scientist
05
Head of Data Science
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