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Data Science Jobs in Raleigh, NC (NOW HIRING)

Elder Research

Data Scientist

Raleigh, NC · On-site +1

Lead Data Science Initiatives: Dive deep into complex and often nebulous requirements, applying expertise in areas such as time series forecasting and Natural Language Processing (NLP) to build ...

Elder Research

Data Scientist

Raleigh, NC · On-site

Lead Data Science Initiatives: Dive deep into complex and often nebulous requirements, applying expertise in areas such as time series forecasting and Natural Language Processing (NLP) to build ...

Elder Research

Data Scientist

Raleigh, NC · On-site +1

Lead Data Science Initiatives: Dive deep into complex and often nebulous requirements, applying expertise in areas such as time series forecasting and Natural Language Processing (NLP) to build ...

Contemporaries

Be Seen First

Data Scientist

Durham, NC · Remote

$51 - $58/hr

Bachelor's degree in Data Science, Computer Science, Statistics, Bioinformatics, Environmental Science, Public Health, or a related field * Minimum of 3 years of professional experience in data ...

Advance Auto Parts, Inc.

Data Scientist

Raleigh, NC · On-site

Work closely with stakeholders to understand business challenges and translate them into data science solutions and work in the end-to-end solutioning. Collaborate with cross-functional teams to ...

Advance Auto Parts

Data Scientist

Raleigh, NC · On-site +1

Work closely with stakeholders to understand business challenges and translate them into data science solutions and work in the end-to-end solutioning. Collaborate with cross-functional teams to ...

Deloitte

AI and Data Science Engineer III

Raleigh, NC

$111K - $133K/yr

AI Data Science Engineer III Our Deloitte Human Capital team transforms technology platforms, drives innovation, and helps make a significant impact on our clients' success. We are hiring a Senior ...

Deloitte

AI and Data Science Engineer III

Raleigh, NC · On-site +1

$111K - $133K/yr

AI and Data Science Engineer III Position Summary Our Deloitte Human Capital team transforms technology platforms, drives innovation, and helps make a significant impact on our clients' success. We ...

Deloitte

Data Scientist

Raleigh, NC

Stay up to date with the latest trends and technologies in data science and machine learning. * Ability to work independently and collaborate as part of a team * Effective written and verbal ...

LexisNexis

Director, Data Sciences

Raleigh, NC · On-site

Define and execute the data science vision and roadmap aligned with business objectives and technological advancement opportunities. * Team Management : Build, lead, and mentor a high-performing team ...

LexisNexis

Director, Data Sciences

Raleigh, NC · On-site +1

Define and execute the data science vision and roadmap aligned with business objectives and technological advancement opportunities. * Team Management : Build, lead, and mentor a high-performing team ...

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Data Science information

See Raleigh, NC salary details

$36.5K

$119.3K

$191K

How much do data science jobs pay per year?

As of Jun 7, 2026, the average yearly pay for data science in Raleigh, NC is $119,305.00, according to ZipRecruiter salary data. Most workers in this role earn between $95,700.00 and $132,200.00 per year, depending on experience, location, and employer.

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

To thrive as a Data Scientist, you need a strong background in statistics, programming (often Python or R), and data analysis, usually supported by a degree in a quantitative field. Familiarity with machine learning libraries (like scikit-learn or TensorFlow), big data tools (such as Hadoop or Spark), and data visualization platforms is typically required. Critical thinking, problem-solving, and effective communication are vital soft skills for translating complex data insights into actionable business strategies. These skills and qualities are essential for extracting value from data, driving informed decisions, and effectively collaborating with multidisciplinary teams.

What are some common challenges faced by data scientists when working with real-world datasets?

Data scientists often encounter challenges such as missing or inconsistent data, unstructured formats, and noisy information in real-world datasets. Cleaning and preprocessing data to ensure its quality can be time-consuming but is critical for building accurate models. Additionally, data scientists may work closely with domain experts and other team members to better understand the data's context and ensure their analyses align with business objectives. Overcoming these challenges requires strong problem-solving skills and effective collaboration within cross-functional teams.

What is data science?

Data science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It combines skills from statistics, computer science, and domain expertise to analyze and interpret complex data sets. Data scientists work with large amounts of data to identify patterns, make predictions, and help organizations make data-driven decisions.

What is the difference between Data Science vs Data Analyst?

AspectData ScienceData Analyst
Required skillsStatistics, programming (Python, R), machine learningData visualization, SQL, basic statistics
Work environmentDeveloping models, predictive analytics, researchReporting, data cleaning, descriptive analysis
Tools usedPython, R, Jupyter, TensorFlowExcel, SQL, Tableau, Power BI
Industry usageTech, finance, healthcare, e-commerceRetail, marketing, finance, healthcare

Data Science and Data Analyst roles often overlap but differ mainly in scope. Data Scientists focus on building predictive models and advanced analytics, requiring programming and machine learning skills. Data Analysts primarily handle data cleaning, reporting, and visualization. Both roles are essential in data-driven industries, but Data Science is more technical and research-oriented, while Data Analysis emphasizes interpreting data for business insights.

What Does a Data Scientist Do?

As a Data Scientist, you are qualified to work in such diverse fields as research and development, politics, advertising and marketing, technology, healthcare, government, and higher education as well as multiple others. In general, your duties and responsibilities will be to compile and analyze relevant statistics and turn those numbers into algorithms that reveal insights that can be used by other researchers in their areas of study. Data Science can reveal things like consumer buying habits or the likelihood of success for a course of action. Other duties might vary, depending on your unique field of specialty. Related areas in which a Data Scientist might wish to focus include work as a Data Analyst, Machine Learning Engineer, and Project Manager.
More about Data Science jobs
What are the most commonly searched types of Data Science jobs in Raleigh, NC? The most popular types of Data Science jobs in Raleigh, NC are:
What are popular job titles related to Data Science jobs in Raleigh, NC? For Data Science jobs in Raleigh, NC, the most frequently searched job titles are:
What job categories do people searching Data Science jobs in Raleigh, NC look for? The top searched job categories for Data Science jobs in Raleigh, NC are:
What cities near Raleigh, NC are hiring for Data Science jobs? Cities near Raleigh, NC with the most Data Science job openings:
Data Science jobs near you
Infographic showing various Data Science job openings in Raleigh, NC as of May 2026, with employment types broken down into 100% Full Time. Highlights an 78% In-person, and 22% Remote job distribution, with an average salary of $119,305 per year, or $57.4 per hour.
Manager (Data Science with AI)

Manager (Data Science with AI)

Datum Software, Inc.

Raleigh, NC

Other

Posted 23 days ago

Job description

Job Details:

Job Title: Manager (Data Science with AI)

Duration: Full Time / Permanent Role

Location: Raleigh, NC || Hybrid

 

Job Description:

Typically requires:

  • 8+ years of relevant experience in data science, machine learning, or applied AI
  • 4+ years of leadership experience (direct or indirect team management)
  • We recognize that exceptional candidates may follow non-traditional paths and value demonstrated impact, technical depth, and leadership over strict credential requirements. Success in this role requires:
  • Leading through both technical expertise and organizational influence
  • Acting as a change agent, embedding best practices into workflows and systems
  • Driving both team development and strategic outcomes across a broad scope
  • Ability to select the right tools and technologies to solve business problems

Technical Proficiency

  • Proficient with Python, ML and LLM tooling such as Google ADK, LangChain, ML Frameworks (e.g. TensorFlow, PyTorch) and prompt tuning techniques.
  • Familiarity with vector databases, knowledge graphs, and hybrid retrieval architecture.
  • Strong experience working with structured and unstructured data at scale.
  • Ability to design and implement data pipelines and preparation workflows.
  • Experience integrating ML into complex, multi-stage processing systems
  • Working knowledge of containerization, CI/CD, RESTful API Design and model serving tools.
  • Cloud infrastructure experience on AWS (preferred), Azure, or Google Cloud Platform.
  • Familiarity with AI Coding Tools (e.g. GitHub CoPilot, Claude Code, OpenAI Codex)

 

Preferred Background

  • Graduate degree in Computer Science, AI, Machine Learning, or equivalent experience.
  • 8+ years of post-degree experience, with 4+ years in a data science or applied AI leadership role, with a focus on NLP/LLM systems.
  • Prior experience in legal tech, legal AI, or document-intensive domains is highly desirable.
  • Familiarity with ethical/legal considerations in deploying generative AI in professional settings.

Key Responsibilities: Scope & Impact

  • Set the vision and strategic priorities, acting as a recognized expert for Data Science
  • Lead and develop a team of data scientists and ML engineers, setting the cultural tone for the group
  • Drive applied research with a clear path to production, explicitly balancing innovation against real-world constraints including latency, cost, and reliability
  • Build and scale evaluation science capabilities within the team, including offline evaluation frameworks, automated benchmarking pipelines, and human-in-the-loop feedback systems to rigorously measure model quality and business impact
  • Champion hands-on rapid prototyping and iteration
  • Collaborate with other Data Science teams to maximize re-use of components and patterns, eliminating waste, duplication and unnecessary customization
  • Operate with broad scope, coordinating across multiple cross-functional teams, systems, and domains

 

Technical & Product Leadership:

  • Collaborate closely with other Data Science teams, to define and execute the AI roadmap across the content lifecycle, maximizing reuse in areas including:
  • Content collection (e.g. "web scraping”) and transformation
  • Metadata extraction, enrichment, and classification
  • Agentic workflows turning real-world events and legal content into legal intelligence
  • AI-powered downstream product capabilities
  • Design and deploy scalable, production-grade AI systems, including:
  • LLM-powered document understanding and generation
  • Agentic workflows balancing agent autonomy and efficiency with required structure and accuracy
  • Retrieval-augmented generation (RAG) pipelines
  • Hybrid ML + rules-based systems for structured content

Lead through execution and by example:

  • Actively writing code, not just delegating
  • Building and demoing working prototypes (e.g. by "vibe coding”)
  • Directly contributing to experiments and production models
  • Establish and scale best practices in Data Science, including:
  • Model development, evaluation, and monitoring
  • Prompt engineering and experimentation frameworks
  • Data preparation and feature engineering standards
  • Reusable components and platform capabilities
  • Partner closely with engineering, architecture, and product leaders to:
  • Integrate AI into large-scale distributed systems
  • Ensure performance, scalability, and reliability
  • Align technical solutions with business outcomes
  • Translate complex, ambiguous problems into clear project plans and executable solutions, and lead teams through delivery
  • Present tradeoffs, alternative approaches and options when faced with delivery constraints

 

Team & Operational Excellence:

  • Mentor and grow a multidisciplinary team of LLM-focused Data Scientists and ML Engineers.
  • Drive cross-functional collaboration with Legal SMEs, Data Engineers, Product Managers, and Design.
  • Establish best practices for evaluation, observability, and responsible use of generative AI.
  • Oversee development of infrastructure to support continuous delivery and monitoring of LLM systems in production environments.

Core Qualifications: Experience & Education

  • Advanced degree (Master''s or PhD) in Data Science, Computer Science, Statistics, or a related field strongly preferred, or equivalent practical experience
  • Bachelor''s degree in a relevant field with significant applied experience in data science, machine learning, or AI

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