is generated, transmitted, and delivered as global energy demands grow. From massive data centers to modernizing transmission systems, our industry-recognized engineers and scientists have been at the forefront of grid t…
Engineering & IT · Louisville, KY
Data Scientist jobs in Louisville, KY
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
282
Open roles today
$35–$88/hr
Typical pay range
$118k
Median, full-time
10
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We are looking for a Desktop Support Analyst to deliver hands-on technical assistance at a manufacturing site in Louisville, Kentucky. This contract opportunity with potential for a permanent role is ideal for someone wh…
Job ID: R235496Posted: 2026-09-14Location: Firstside Center Bldg (PA373); PNC Tower - Louisville (KY012)Type: Full timeCountry: United States of AmericaCompany: PNCPosition OverviewAt PNC, our people are our greatest dif…
CompanyBrightSpring Health ServicesOverviewThe Human Resources Information Systems Analyst is responsible for supporting operational data needs in our HR applications including ADP Enterprise, Cornerstone, iCIMS and any…
$129.3k - $177.8k
engineering, and productionization of AI-enabled automation across the Data Governance organization. This role is focused on building... ...training foundational models or serving as a traditional data scientist. The eng…
ResponsibilitiesThe Senior Clinical Applications Systems Analyst Epic independently defines system objectives, requirements, scope, and impact based on needs of clinical operations. Develops or modifies clinical informat…
ResponsibilitiesThe Manager, Data, CRM & Analytics is responsible for the general management and integrity of the Foundation's fundraising donor database, online giving, and related fundraising technology. This position…
$118.7k - $243.7k
Work you’ll do Assist in the design, implementation, and sustainment of zero trust architectures to safeguard critical assets and data against emerging cyber threats.Serve as the subject matter expert (SME) for applicati…
Job-ID27380861Reference26-03163Lead Data Scientist - Autonomous Goal ManagementJob Description SummaryThe Enterprise AI organization at client is a pioneering force, driving AI innovation across our Insurance and CenterW…
DescriptionAt HDR, our employee-owners are fully engaged in creating a welcoming environment where each of us is valued and respected, a place where everyone is empowered to bring their authentic selves and novel ideas t…
Claude AI Data Engineer Location: Remote Job Type: Contract Job Overview We are seeking a Data Engineer with experience using Claude AI / Generative AI technologies to support data engineering, data processing, automatio…
Legacy Data Engineer Ab Initio / Netezza Location: Remote Job Type: Contract Job Overview We are seeking a Legacy Data Engineer with strong experience in ETL development and legacy data technologies. The ideal candidate…
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What data scientists earn in Louisville
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $35–$48 | $73k–$100k |
| Mid level | $48–$65 | $100k–$136k |
| Senior | $63–$88 | $132k–$182k |
Adjusted for the Louisville 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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