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Computer Science Data Science Jobs in North Carolina

Fidelity Investments

Director, Data Science

Durham, NC · On-site

Bachelor's degree in Analytics, Computer Science, Data Science, Operations Research, Economics, or a closely related field (or foreign education equivalent) and six (6) years of experience as a ...

Fidelity Investments

Director, Data Science

Durham, NC · On-site

Bachelor's degree in Analytics, Computer Science, Data Science, Operations Research, Economics, or a closely related field (or foreign education equivalent) and six (6) years of experience as a ...

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All JobsComputer Science Data Science JobsComputer Science Data Science Jobs in North Carolina

Computer Science Data Science information

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$34.1K

$111.5K

$178.6K

How much do computer science data science jobs pay per year?

As of Jul 9, 2026, the average yearly pay for computer science data science in North Carolina is $111,545.00, according to ZipRecruiter salary data. Most workers in this role earn between $89,500.00 and $123,600.00 per year, depending on experience, location, and employer.

Is data science high paying?

Data science roles, including those in computer science and data science, are generally high paying due to the specialized skills required, such as programming, statistical analysis, and machine learning. Salaries tend to be above average compared to many other tech positions and often increase with experience, certifications, and advanced skills in tools like Python, R, and SQL.

Is 40 too late for data science?

Computer Science Data Science is a field where individuals can enter at any age, including 40, as long as they develop relevant skills such as programming, statistics, and machine learning. Many professionals successfully transition into data science later in their careers by gaining certifications, building portfolios, and gaining practical experience.

Can computer science majors get data science jobs?

Yes, computer science majors often qualify for data science roles because they typically have strong programming, statistical, and analytical skills. Success in obtaining a data science job may also depend on experience with tools like Python, R, and SQL, as well as knowledge of machine learning and data visualization. Additional certifications or projects can enhance employability in this field.

Will AI replace data science?

AI is transforming data science by automating tasks like data analysis and model development, but it is unlikely to fully replace data scientists. Instead, data scientists will increasingly focus on interpreting AI outputs, developing new algorithms, and applying domain expertise. Skills in programming, statistical analysis, and machine learning tools remain essential for the role.
What are popular job titles related to Computer Science Data Science jobs in North Carolina? For Computer Science Data Science jobs in North Carolina, the most frequently searched job titles are:
What job categories do people searching Computer Science Data Science jobs in North Carolina look for? The top searched job categories for Computer Science Data Science jobs in North Carolina are:
What cities in North Carolina are hiring for Computer Science Data Science jobs? Cities in North Carolina with the most Computer Science Data Science job openings:
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Director, Data Science

Director, Data Science

Fidelity Investments

Durham, NC • On-site

Full-time

Retirement

Posted 29 days ago

Fidelity Investments rating

8.7

Company rating: 8.7 out of 10

Based on 266 frontline employees who took The Breakroom Quiz

16th of 146 rated financial services

Job description


Position Description:
Leads and oversees end-to-end data science initiatives, guiding teams through data cleansing, preparation, annotation, feature engineering, exploratory analysis, and model development. Provides strategic direction on Machine Learning (ML) pipeline architecture, ensures alignment with business objectives, and drives cross-functional collaboration to deliver scalable, high-impact solutions. Draws on in-depth knowledge of the business or function to provide business unit-wide solutions by building, testing and monitoring AI models. Researches and recommends new technologies, and seizes opportunities by staying abreast of publications, tools, and techniques from the global Artificial Intelligence (AI/ML) community, in support of the strategic direction of the business unit and to achieve business-unit-wide solutions.
Primary Responsibilities:
  • Identifies business opportunities and evaluates best approaches for predictive or prescriptive analytics.
  • Implements best practices for model development, iteration, as well as code management and conducts code reviews.
  • Draws key business insights from advanced quantitative analyses and presents findings to broader audience.
  • Leads the design and deployment of advanced analytics solutions that convert raw data into actionable intelligence.
  • Delivers scalable insights, while aligning analytics infrastructure with business priorities.
  • Directs the development and integration of analytics frameworks that transform raw data into strategic insights.
  • Ensures solutions are scalable, business-aligned, and drive data-informed decision-making across the organization.
  • Leads and oversees the full AI/ML lifecycle -- data ingestion, model development, training, deployment, and monitoring.
  • Identifies and consults with internal and external technical resources to produce cross-company strategic designs.
  • Consults on deployment of major project deliverables.
  • Initiates and drives project or strategy discussions with users or external groups to resolve issues.
  • Sets vision, goals, and direction of team/organization.
  • Plans and leads organization-wide initiatives.
  • Provides leadership, technical supervision, and expertise to multiple teams in broad technical areas on complex organization-wide projects.
  • Advises senior management on technical strategy.
  • Regularly provides guidance, training, and coaching to other team members for performance and career development.
  • Identifies and plans for future resource needs.

Education and Experience:
Bachelor's degree in Analytics, Computer Science, Data Science, Operations Research, Economics, or a closely related field (or foreign education equivalent) and six (6) years of experience as a Director, Data Science (or closely related occupation) designing and building complex and scalable Artificial Intelligence (AI) pipelines to improve customer experience and drive business results in the financial services industry.
Or, alternatively, Master's degree in Analytics, Computer Science, Data Science, Operations Research, Economics, or a closely related field (or foreign education equivalent) and four (4) years of experience as a Director, Data Science (or closely related occupation) designing and building complex and scalable Artificial Intelligence (AI) pipelines to improve customer experience and drive business results in the financial services industry.
Skills and Knowledge:
Candidate must also possess:
  • Demonstrated Expertise ("DE") developing supervised and unsupervised Machine Learning (ML) algorithms -- regression, gradient boosting trees/random forest, neural network, feature selection/reduction, clustering, and parameter tuning -- using R, Python, and SAS programming languages; and analyzing and evaluating model results by creating data visualizations and business intelligence reports in Tableau and Adobe Analytics.

  • DE performing data wrangling and feature engineering for large, complex data across Cloud and on-premise data warehouses -- Oracle, Greenplum/Postgres, Hadoop/Hive, Snowflake, S3, and Redis -- using SQL, Python, and database specific SQL; standardizing and optimizing complex queries using database techniques -- partitioning and parallel processing; aggregating time series and transaction tables; creating appropriate features for modeling out of structured and unstructured data; detecting and preventing data leakage and model biases through model fairness measures using open-source AI fairness and ethics libraries.

  • DE analyzing technology solutions for supporting model deployment and integration in Cloud and on premise environments; and building model deployment and integration workflows on Amazon Web Services (AWS), on-premise Hadoop, and UNIX platforms through Git, Jenkins, Python scripts, cron jobs, step functions, Docker images, and APIs.

  • DE migrating existing AI/ML processes from on-premise environments to AWS platforms, using Extract- Transform-Load (ETL) procedures, Python, and Docker containers; creating data quality guardrails to validate model inputs and outputs using ICEDQ; and addressing financial services Cloud security constraints and record systems for workplace services -- 401(K), defined benefits, and workplace compensation and retirement plans, using AWS security tools.

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Certifications:
Category:
Data Analytics and Insights
Please be advised that Fidelity's business is governed by the provisions of the Securities Exchange Act of 1934, the Investment Advisers Act of 1940, the Investment Company Act of 1940, ERISA, numerous state laws governing securities, investment and retirement-related financial activities and the rules and regulations of numerous self-regulatory organizations, including FINRA, among others. Those laws and regulations may restrict Fidelity from hiring and/or associating with individuals with certain Criminal Histories.

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