$104k - $208k
Salary: $104,000 - 208,000 per year Requirements: We require native or bilingual-level fluency in English. We require proficiency in at least one programming language or framework, such as JavaScript, TypeScript, Python,…
Engineering & IT · Oklahoma City, OK
Data Scientist jobs in Oklahoma City, OK
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
313
Open roles today
$34–$86/hr
Typical pay range
$116k
Median, full-time
2
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01
Open data scientist roles
11 shown of 313 · sorted by freshness
Req ID: 487988 Welcome to Love's! Our FP&A Department is seeking a Business Intelligence Analyst who will analyze data and create impactful visuals to support strategic decision-making. By integrating multiple data sourc…
$51.9k - $74.2k
and technical personnel to define, track and communicate business requirements and their expected impact by building basic conceptual data and process models Create, execute or analyze basic test scenarios to verify that…
Analyst to join our Workforce Management team. This role is ideal for someone who thrives at the intersection of HR, Operations and data, and who can turn workforce data into actionable insights that drive business decis…
Competitive Pay * Career Development * Welcome to Love's: The Data Engineer II designs, builds, and supports scalable data... ...II partners with Technology teams, business stakeholders, data scientists, data analysts, v…
Analytics and plays an important part in uncovering customer insights, crafting meaningful visuals, monitoring performance, and enabling data- driven decision-making across customer acquisition, retention, and value. The…
$70k - $90k
Job Title: Data Solutions Developer Location: Oklahoma City, OK Pay Range : $70,000 - $90,000 / year depending on experience Benefits: The position is eligible for medical, dental, vision, and 401(k). ** Onsite requireme…
Dental/Vision Insurance the first of the month after 30 days * Competitive Pay * Career Development * Welcome to Love's: The Data Scientist plays a critical role in advancing Love’s customer analytics capabilities by dri…
Data Engineer Location: Onsite in OKC – 5 days/week Employment Type: Direct Hire Work Authorization: Must be authorized to work in the U.S. now and in the future without sponsorship We’re partnering with a client on a di…
$95k - $105k
database tables, views, indexes, and stored procedures in Microsoft SQL Server • Develop, optimize, and maintain SSIS packages for data import and transformation processes • Ensure data integrity and consistency througho…
Database Architect / Administrator We’re looking for someone to join our team with at least five years of experience in relational databases and a minimum of two years of experience in SQL Server. Essential duties and re…
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What data scientists earn in Oklahoma City
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $34–$47 | $71k–$98k |
| Mid level | $47–$64 | $98k–$134k |
| Senior | $62–$86 | $129k–$178k |
Adjusted for the Oklahoma 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
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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
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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