$123k - $163k
Computer Science, Engineering, Information Systems, or another related technical field. We are looking for 2–5 years of experience in data engineering, analytics engineering, production support, or a similar discipline.…
Engineering & IT · St. Louis, MO
Data Scientist jobs in St. Louis, 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.
977
Open roles today
$36–$88/hr
Typical pay range
$120k
Median, full-time
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12 shown of 977 · sorted by freshness
Kforce's client in Chesterfield, MO is seeking a Data Engineer to join a global Smart Manufacturing Solutions team responsible for connecting operational technology (OT) environments with enterprise data platforms. This…
Job Description Job Description Data Annotation Quality Control Analyst About Enabled Intelligence, Inc. Enabled Intelligence, Inc. provides extremely accurate, precise and secure data labeling and AI solutions to help o…
Summary Spire is seeking to fill a Data Engineer II position. This role is a primary contributor and designer of the overall Spire data warehouse, data ETL processes and analytics data model and architecture. This role i…
Job Description Job Description We're partnering with an established manufacturing organization seeking a Data Engineer to support enterprise data integration and analytics initiatives. This role focuses on building and…
Some problems don’t need more data, they need clarity. At the National Geospatial-Intelligence Agency (NGA) Office of Eurasia, leadership... ...is an exciting opportunity to join us as a Geospatial Data Scientist to supp…
Job Description Job Description What Impact You'll Have We are seeking a Senior Data Scientist to support mission-driven analytic and modernization efforts across the Intelligence Community. This role leverages advanced…
Job Description Job Description iQuasar is seeking to fill a Data Engineer in St. Louis, MO . Position: Data Engineer Location: St. Louis, MO ( Onsite Mon-Fri) - Travel expenses will be paid Clearance: Secret Role Overvi…
Overview Help power mission-critical systems through enterprise data. As a Technical Consultant, Enterprise Data Engineer, you will design and implement scalable geospatial data platforms that support Defense and Intelli…
Job Description Job Description Tiger Analytics is looking for experienced Data Scientists to join our fast-growing advanced analytics consulting firm. Our consultants bring deep expertise in Data Science, Machine Learni…
$160k - $175k
Job Description Job Description Senior Exploitation Specialist / Data Scientist Full Time Springfield, VA; Arnold, MO; Tampa, FL About Sphinx Sphinx is a full spectrum security and intelligence company established by a g…
programs to one of the most culturally diverse student bodies in the St. Louis region. Job Summary: We are seeking a talented Data Scientist to analyze data from our research on the effects of light pollution on pregnanc…
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What data scientists earn in St. Louis
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $36–$49 | $74k–$101k |
| Mid level | $49–$66 | $101k–$138k |
| Senior | $64–$88 | $133k–$184k |
Adjusted for the St. Louis 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
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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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