Senior Data Scientist - Applied Machine Learning Remote 3-6+ Months Hands-on senior technical resource on a two-person KCS project team focused on developing an ML solution for identifying high-value Cisco Learning engag…
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.
492
Open roles today
$36–$88/hr
Typical pay range
$120k
Median, full-time
8
Fresh in this list
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01
Open data scientist roles
12 shown of 492 · sorted by freshness
$123k - $163k
services concepts and terminology, particularly in wealth management ~ Proficient in T-SQL, relational databases, and foundational data modeling principles ~ Ability to write SQL queries that aggregate, analyze, and stru…
$70 per hour
job summary: The Senior AI Platform Engineer will support the development of the company's AI platform, working closely with the Lead AI Platform Engineer to enhance chatbot and agentic AI capabilities. The role will als…
Working Ameren Title: Senior AI Platform Engineer Day-to-day: The Senior AI Platform Engineer will support the development of the company's AI platform, working closely with the Lead AI Platform Engineer to enhance chatb…
provider headquartered in Ann Arbor, Michigan that offers strategic talent solutions to our clients world-wide. Job Title: IT Data Engineer Location: St Louis, MO, 63146 Duration: 12 Months Job Type: Temporary Assignment…
Job Title:AI/ML Developer Location: St Louis (4 days onsite/week) (Candidates in STL preferred) JD: We are looking for a skilled AI/ML Developer to join our agentic platform team. You will design, develop, and deploy LLM…
Job Title: Databricks Data Engineer ( Databricks, AWS/Azure, Snowflake ) - St. Louis Primary Location: St. Louis, MO Work... ..., lineage tracking, and compliance Collaborate with data scientists and analytics teams Supp…
$180k - $208k
~7+ years of experience in designing and building intricate data processing pipelines and streaming solutions. ~ Familiarity with... ...developers, data engineers, database architects, analysts, and data scientists to en…
Job Title: Senior Electrical Engineer Data Center on w2(No c2c candidate) Location: 5 days onsite at Overland Park, KS, Ann Arbor, MI, Bloomington, MN, Canonsburg, PA, Cary, NC, Dallas, TX, Darrien, IL, Denver, CO, Houst…
$55.4k - $113k
working world by creating new value for clients, people, society and the planet, while building trust in capital markets. Enabled by data, AI and advanced technology, EY teams help clients shape the future with confidenc…
$88.2k - $198k
Senior Full Stack Data Engineer Position Description This position must be performed on-site, hybrid in St. Louis, MO. Join a team responsible for designing and delivering cloud-based applications that support complex bu…
We are seeking a Data Scientist with strong experience in advanced analytics, statistical modeling, machine learning, and artificial intelligence. The ideal candidate will be responsible for analyzing complex structured…
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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
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.
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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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