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Engineering & IT · Charlotte, NC
Data Scientist jobs in Charlotte, NC
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
955
Open roles today
$38–$94/hr
Typical pay range
$127k
Median, full-time
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Fresh in this list
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Open data scientist roles
11 shown of 955 · sorted by freshness
As a Data Scientist II here at Honeywell, you will leverage your expertise in audit analytics, data engineering, risk and fraud analytics, predictive modeling, and advanced AI technologies to drive impactful data science…
workflows o Retrieval Augmented Generation (RAG) o Generative AI concepts • Familiarity with AI governance, explainability, and trusted data practices. ________________________________________ Oracle AI Experience • Expe…
Job-ID29304584Reference26-26003In this contingent resource assignment, you may: Consult on complex initiatives with broad impact and large-scale planning for Database Engineering. Review and analyze complex multi-faceted…
$156k - $234k
Dir Data Engineering - GE06AEWe’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages... ...assessment preferred.Ability to partner with actuaries, data scientists, a…
$132.26k - $155.6k
career. Try new things, learn new skills and discover what you excel at—all from Day One.Job DescriptionJob Duties -Responsible for big data/ analytics projects that gather and integrate large volumes of data. -Specializ…
Job-ID29186541Reference26-24519Remote50% RemoteIn this contingent resource assignment, you may: Consult on or participate in moderately complex initiatives and deliverables within Software Engineering and contribute to l…
About this role: Wells Fargo is seeking a Software Engineering Senior Manager - Quantitative Data & Analytics to lead a team of engineering professionals supporting the modernization and transformation of the Wealth & In…
Managing Director, Data Engineering & AIWho You'll Work WithAs a Managing Director in Slalom's Data & AI practice, you will lead the growth of our Data Engineering & AI business by helping clients modernize their data fo…
$80.46k - $142.72k
deliver more impact together.Role description:As an Electrical Engineer you will lead the electrical discipline of multiple concurrent data center projects through pursuit, proposal, design, and construction phases. You…
Must Have Skills Strong experience in AWS services (hands-on experience) Data engineering skills (Glue, ECS, Data pipeline etc,) primarily serverless, databases, storage services, container services, schedulers, and batc…
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What data scientists earn in Charlotte
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $38–$52 | $78k–$108k |
| Mid level | $52–$71 | $108k–$147k |
| Senior | $68–$94 | $142k–$196k |
Adjusted for the Charlotte 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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