ZipRecruiter

Log In

1

Data Science Developer Jobs in North Carolina (NOW HIRING)

ADUSA Distribution

Manager Data Science

Salisbury, NC · On-site

Required : • Bachelor's degree in industrial engineering, Operations Research, Supply Chain ... Science, or a related quantitative field required. • 7+ years of progressive experience in ...

Socket.dev

Data Scientist

Fort Liberty, NC · On-site

$100 - $150/hr

Perform data engineering, data processing and modeling techniques using cloud-based data management, data science, and ML platforms such as Databricks, IBM Cloud Pak, Cloudera, and Snowflake.

Munich Re

Data Scientist

Charlotte, NC · On-site +1

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Act as a liaison between the ML Engineers and Data Science team, managing AI/ML model deployments and conducting testing. * Participate in the development of modeling presentations and present ...

Centraprise

Data Science Tester

Charlotte, NC · On-site

Data Science Tester Job Location : Charlotte, NC (ONSITE) Job Type : Full-Time Must Have Technical ... Nice to Haves • Experience with large-scale enterprise analytics or developer productivity ...

previous page

Showing results 41-60

All JobsData Science Developer JobsData Science Developer Jobs in North Carolina

Data Science Developer information

See North Carolina salary details

$15

$51

$74

How much do data science developer jobs pay per hour?

As of Aug 16, 2026, the average hourly pay for data science developer in North Carolina is $51.63, according to ZipRecruiter salary data. Most workers in this role earn between $42.40 and $61.15 per hour, depending on experience, location, and employer.

Is data science highly paid?

Data science developers are generally well-paid due to high demand for skills in statistical analysis, machine learning, and programming languages like Python and R. Salaries vary by experience, location, and industry, but data science roles often offer competitive compensation compared to other tech positions.

What are some common challenges data science developers face when integrating models into production environments?

Data Science Developers often encounter challenges in bridging the gap between developing models in experimental settings and deploying them into scalable, reliable production systems. Issues such as data inconsistencies, version control, and ensuring model reproducibility can arise. Additionally, collaborating effectively with DevOps and engineering teams to automate deployment pipelines and monitor model performance is crucial for long-term success. Understanding both machine learning and software engineering best practices helps overcome these hurdles and ensures smooth, efficient integration.

What is a data science developer?

A Data Science Developer is a professional who combines expertise in programming, statistics, and data analysis to build models and algorithms that extract insights from large datasets. They often work with machine learning techniques, data visualization, and various data processing tools to solve business problems. Data Science Developers collaborate with other teams to design and implement data-driven solutions, and are skilled in languages like Python, R, or Scala. Their work is essential for organizations looking to leverage data for decision-making and innovation.

What are the key skills and qualifications needed to thrive as a data science developer, and why are they important?

To thrive as a Data Science Developer, you need strong expertise in statistics, machine learning, programming (Python, R), and a background in computer science or a related quantitative field. Familiarity with data analysis tools (such as Pandas, NumPy, and scikit-learn), cloud platforms (like AWS or Azure), and experience with databases (SQL/NoSQL) are typically required, and certifications like Microsoft Certified: Azure Data Scientist Associate can be beneficial. Strong problem-solving skills, effective communication, and the ability to collaborate with cross-functional teams help you stand out. These skills are essential for building data-driven solutions that translate complex data into actionable business insights.

Is data science hard to learn?

Data science is a complex field that requires learning skills in programming, statistics, and data analysis, which can be challenging for beginners. Success often depends on prior knowledge, dedication, and practice with tools like Python or R, as well as understanding data concepts. While it can be difficult initially, consistent effort and training can make it manageable for many learners.

What is the difference between Data Science Developer vs Data Analyst?

AspectData Science DeveloperData Analyst
Required SkillsProgramming, machine learning, data modelingData visualization, statistical analysis, reporting
CertificationsData Science certifications, Python/R expertiseExcel, SQL, Tableau certifications
Work EnvironmentTech companies, startups, R&D teamsBusiness departments, marketing, finance
Job FocusDeveloping algorithms, predictive modelsInterpreting data, generating reports

While both roles analyze data, Data Science Developers focus on building models and algorithms to solve complex problems, often requiring programming and machine learning skills. Data Analysts primarily interpret existing data sets to generate insights and reports for business decisions. The roles overlap in data handling but differ in technical depth and objectives.

Is data science a growing career?

Data science is a rapidly expanding field with increasing demand for professionals skilled in statistical analysis, machine learning, and data visualization. As organizations rely more on data-driven decision-making, roles like data science developers are expected to grow significantly in the coming years.

What are popular job titles related to Data Science Developer jobs in North Carolina?

For Data Science Developer jobs in North Carolina, the most frequently searched job titles are:

What job categories do people searching Data Science Developer jobs in North Carolina look for?

The top searched job categories for Data Science Developer jobs in North Carolina are:

Data Science Developer jobs near you
Infographic showing various Data Science Developer job openings in North Carolina as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 14% Part Time, and 3% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $107,394 per year, or $51.6 per hour.

Data Science Tester

Long Finch Technologies

Mcadenville, NC

Full-time

Posted 17 days ago

Job description

We are looking for an experienced Data Science Tester / Analytics Specialist to validate enterprise analytics solutions and ensure data accuracy, reliability, and meaningful insights. The ideal candidate will have strong expertise in SQL, MongoDB, Power BI, and statistical analysis with experience testing and validating complex data models and metrics.

Key Responsibilities
  • Validate analytics metrics and dashboards across Oracle SQL, MongoDB, and Power BI.
  • Perform data reconciliation, quality checks, and analytical validation.
  • Write advanced SQL queries using joins, CTEs, and window functions.
  • Validate Power BI data models, DAX calculations, refresh processes, and reports.
  • Analyze trends, anomalies, data distributions, and reporting accuracy.
  • Collaborate with data engineers, BI developers, and business teams to improve metric reliability.
  • Document findings, assumptions, and analytical recommendations.
Required Skills
  • 10+ years of experience in data analytics, data testing, or BI validation.
  • Strong SQL skills (Oracle preferred).
  • Hands-on experience with MongoDB and aggregation pipelines.
  • Strong Power BI knowledge including data modeling and DAX.
  • Understanding of statistical analysis, data quality, and analytics validation.
  • Experience with Agile/Scrum and tools like Jira.
  • Excellent communication skills.
Preferred Skills
  • Experience with enterprise analytics platforms and software engineering metrics.
  • Knowledge of BI semantic modeling (Star/Snowflake schema).
  • Python scripting for data analysis and automation.
  • Exposure to tools like Jenkins, SonarQube, or Ansible Tower.

Apply