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Computer Science Data Jobs in North Carolina (NOW HIRING)

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 ...

Elder Research

Data Scientist

Raleigh, NC · On-site +1

Bachelor of Science degree in a relevant field such as statistics, computer science, economics, mathematics, analytics, data science, business, or social sciences * Proficiency in Python, including ...

Elder Research

Data Scientist

Raleigh, NC · On-site

Bachelor of Science degree in a relevant field such as statistics, computer science, economics, mathematics, analytics, data science, business, or social sciences * Proficiency in Python, including ...

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Computer Science Data information

What are some common challenges faced by professionals working in computer science data roles, and how can they be addressed?

Professionals in computer science data roles often encounter challenges such as handling large and complex datasets, ensuring data quality, and keeping up with rapidly evolving technologies. Collaboration with cross-functional teams is essential, as data professionals frequently work with engineers, analysts, and business stakeholders to interpret data and deliver actionable insights. To address these challenges, it's important to invest in continuous learning, leverage automation tools for data cleaning, and maintain clear communication channels within the team.

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

AspectComputer Science DataData Analyst
Required CredentialsBachelor's or higher in Computer Science, Data Science, or related fieldsBachelor's degree in Statistics, Mathematics, or related fields
Work EnvironmentSoftware development, data engineering, algorithm designData interpretation, reporting, visualization
Employer & Industry UsageTech companies, startups, research institutionsBusiness, finance, marketing, healthcare
Common Search & ComparisonOften compared for data handling and programming skillsCompared for data interpretation and business insights

Computer Science Data professionals focus on developing algorithms, managing data systems, and building software solutions. Data Analysts primarily interpret data, create reports, and provide insights for decision-making. While both roles work with data, their core responsibilities and skill sets differ significantly.

What are the key skills and qualifications needed to thrive as a Data Scientist in Computer Science, and why are they important?

To thrive as a Data Scientist in Computer Science, you need strong analytical skills, proficiency in statistics, and a solid background in programming (often with a degree in computer science, mathematics, or a related field). Familiarity with tools like Python, R, SQL, and machine learning frameworks, as well as certifications such as AWS Certified Data Analytics or Google Data Engineer, are highly valuable. Excellent problem-solving abilities, communication skills, and curiosity help you interpret data insights and explain findings to non-technical stakeholders. These skills ensure you can extract actionable insights from complex data, drive business decisions, and collaborate effectively within multidisciplinary teams.

What is computer science data?

Computer science data refers to the information that is processed, analyzed, and utilized within the field of computer science. This data can include anything from raw numbers and text to images, audio, and video, and is often used in programming, machine learning, artificial intelligence, databases, and data analysis. Understanding how to collect, store, structure, and interpret data is a fundamental skill for computer scientists and is crucial for solving real-world problems using technology.
Computer Science Data jobs near you
Infographic showing various Computer Science Data job openings in North Carolina as of June 2026, with employment types broken down into 1% As Needed, 81% Full Time, 14% Part Time, 1% Temporary, and 3% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution.

Director, Data Science

Fidelity Investments

Durham, NC • On-site

Full-time

Retirement

Posted 8 days ago

Fidelity Investments rating

8.7

Company rating: 8.7 out of 10

Based on 264 frontline employees who took The Breakroom Quiz

14th of 138 rated financial services

Job description

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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