Job Title: Senior Data Engineer Location: Charlotte, NC Job Type: Long-Term Contract Top Skills: Python | SQL | AWS/Azure | Databricks | Spark/PySpark | ETL/ELT | Data Pipelines Job Description: We are seeking a Senior D…
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.
1,886
Open roles today
$38–$94/hr
Typical pay range
$127k
Median, full-time
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Open data scientist roles
12 shown of 1,886 · sorted by freshness
Job Title: Data Engineer Location: Charlotte, NC (Hybrid) Type: Contract Job Description: This role involves working with cross-functional teams to develop, manage, and optimize cloud-based data solutions for cybersecuri…
Lead Data Engineer Location: Charlotte, NC 28273 (100% Onsite 5 Days/Week) Position Type: 6 month Contract-to-Hire Interview : Virtual and final in-person Key Skills: Azure Databricks, Data Engineer, PySpark, Python, SQL…
Hello Functional Job Title: Lead Data Engineer Location (City State, Street Name): Charlotte NC 28273 Large Healthcare organization is seeking a lead Data Engineer to join their expanding team This is a contract to hire…
Only Experience: 10 years + Role Description Client is undergoing a digital transformation to modernize its enterprise data platform across Capital Markets . As part of this initiative, we are seeking an experienced Data…
Job Title Sr. Snowflake Data Engineer Job Location Charlotte, NC | Lewisville, TX | Jacksonville, FL Hybrid (3 days a week from Office) Job Duration: Long Term Required Skills: Snowflake Data Engineering Data Modeling &…
Role: Lead Data Engineering Location: Charlotte, North Carolina Job Description: 8+ years of Technology Delivery / Program Management / Technical Project Leadership experience 5+ years delivering cloud-based Data & Analy…
We are seeking a Lead Data Engineer with strong experience in delivering enterprise-scale cloud data and analytics solutions. The ideal candidate will have expertise in Google Cloud Platform (GCP), technical project lead…
$60 - $66 per hour
job summary: The primary role of the Data Engineer is to function as a critical member of a data team by designing data integration solutions that deliver business value in line with the company's objectives. They are re…
$60 per hour
job summary: We are seeking a highly skilled Data Engineer with strong experience in AWS-based data pipelines and modern data engineering practices. The ideal candidate will be hands-on with Python/PySpark, possess solid…
Job Description Job Description No C2C or third parties Fulltime W2 Prefer Charlotte NC Data Engineer Company is looking for an experienced Mid-Level Data Engineer to design, build, and operate scalable data pipelines an…
Job Description Job Description Senior Data Engineer – Data Architecture & Platform CPI Security, a national leader in residential and commercial security solutions, is seeking a Senior Data Engineer transitioning into D…
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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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