critical infrastructure that supports the digital age and shapes the spaces where people work, connect, and thrive. From high-performance data centers driving the future of AI to dynamic commercial environments, your wor…
Engineering & IT · Columbus, OH
Data Scientist jobs in Columbus, OH
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
1,020
Open roles today
$37–$91/hr
Typical pay range
$124k
Median, full-time
9
Fresh in this list
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01
Open data scientist roles
12 shown of 1,020 · sorted by freshness
$61k - $101k
Requirements: Formal training or certification in software engineering concepts, with at least 3 years of applied experience Strong data modeling expertise, including modeling tables with ERwin and applying normalization…
$136.6k - $184.8k
delivery, and operation of all AWS global infrastructure. In other words, we’re the people who keep the cloud running. We support all AWS data centers and all of the servers, storage, networking, power, and cooling equip…
$95.5k - $177.5k
is responsible for leading test planning, test execution, and data validation activities that support delivery of high-quality data... ...truth across all data consumers, including business users, data scientists, and Te…
$98.18k - $115.5k
At U.S. Bank, we’re on a journey to do our best. Helping the customers and businesses we serve to make better and smarter financial decisions and enabling the communities we support to grow and succeed. We believe it tak…
Job Description As a Data Scientist Sr. Associate within the Consumer Bank Marketing Analytics team, you will be at the center of understanding what drives account opening performance across checking, savings, and certif…
Engineer, we'll count on you to:Lead the Electrical team and collaborate with multidisciplinary project teams throughout all phases of data center and other industrial projects to develop and deliver high-quality enginee…
leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global de…
Position: Software Engineer 4 - ContingentLocation: Columbus, OhioDuration: ContractJob ID: 180071Job Overview: In this contingent resource assignment, you will consult on complex initiatives with broad impact and large-…
Job Description As a Lead Data Engineer (Forward Deployed) at JPMorganChase within the Infrastructure Data Platforms team, you will embed with infrastructure product teams to turn how asset and configuration data exists…
Job Summary We are seeking a detail-oriented Data Analyst to collect, analyze, and interpret data to support business decision-making. The ideal candidate will have strong analytical skills and experience working with da…
Seeking a Data Center Engineer to deploy, maintain, and support enterprise data center infrastructure. The ideal candidate will have experience with structured cabling, hardware installation, network infrastructure, trou…
02
What data scientists earn in Columbus
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $37–$50 | $76k–$104k |
| Mid level | $50–$68 | $104k–$142k |
| Senior | $66–$91 | $138k–$190k |
Adjusted for the Columbus 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.
03
Distinguish shipped work from research; 'model serving 2M users' and 'notebook analysis' are different claims.
04
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
02
Data Scientist
03
Senior Data Scientist
04
Staff Data Scientist
05
Head of Data Science
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