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Financial Data Scientist Jobs in Raleigh, NC (NOW HIRING)

NetApp

Data Product Architect

Morrisville, NC · Hybrid

$59.75 - $76.75/hr

... science, machine learning, and AI tools for coding, SQL generation, data modeling, and ... financial data and operational controls. Job Requirements Expertise in SQL, RESTful APIs, and ...

Envestnet

Lead Data Engineer

Raleigh, NC

... financial advice, visit www.envestnet.com.      The Team You'll Join   The Lead Data ... in Computer Science or relevant field, Masters Degree is a plus. Why You'll Enjoy Working at ...

Envestnet

Lead Data Engineer

Raleigh, NC

$111K - $133K/yr

... financial advice, visit www.envestnet.com.      The Team You'll Join   The Lead Data ... Bachelor Degree in Computer Science or relevant field, Masters Degree is a plus. Why You'll Enjoy ...

Envestnet

Lead Data Engineer

Raleigh, NC

$99K - $131K/yr

... financial advice, visit www.envestnet.com.      The Team You'll Join   The Lead Data ... Bachelor Degree in Computer Science or relevant field, Masters Degree is a plus. Why You'll Enjoy ...

Dlapiper

Sr Data Analyst

Raleigh, NC · Hybrid

$83K - $105K/yr

Bachelor's degree in Data Science/Analytics, Computer Science, Finance, Economics, Accounting or related field. Minimum Years of Experience * 4 years' experience in a experience as a Data Analyst or ...

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All JobsFinancial Data Scientist JobsFinancial Data Scientist Jobs in Raleigh, NC

Financial Data Scientist information

See Raleigh, NC salary details

$36.5K

$119.3K

$191K

How much do financial data scientist jobs pay per year?

As of Aug 8, 2026, the average yearly pay for financial data scientist in Raleigh, NC is $119,312.00, according to ZipRecruiter salary data. Most workers in this role earn between $95,800.00 and $132,200.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a financial data scientist?

To thrive as a Financial Data Scientist, you need strong quantitative skills, proficiency in statistical analysis, and a background in finance or economics, typically supported by a relevant degree. Familiarity with programming languages such as Python or R, experience with machine learning frameworks, and knowledge of financial databases and tools like Bloomberg Terminal are also important. Critical thinking, problem-solving, and effective communication help you translate complex data into actionable insights for stakeholders. These skills are crucial for building accurate financial models, driving data-driven decision-making, and delivering value in dynamic financial environments.

What is the difference between Financial Data Scientist vs Quantitative Analyst?

AspectFinancial Data ScientistQuantitative Analyst
Required CredentialsDegree in Finance, Data Science, or related fields; often certifications like CFA or FRMDegree in Mathematics, Statistics, Finance; CFA or FRM common
Work EnvironmentFinancial institutions, tech firms, investment firms; focus on data modeling and predictive analyticsInvestment banks, hedge funds, asset management; focus on trading strategies and risk modeling
Employer & Industry UsageUsed across finance and tech sectors for data-driven decision makingPrimarily in finance for trading, risk, and portfolio management

Financial Data Scientists analyze large datasets to develop predictive models and insights, often combining finance knowledge with data science skills. Quantitative Analysts focus on developing mathematical models for trading and risk management. While both roles require strong quantitative skills and finance knowledge, Financial Data Scientists tend to work more on data analysis and machine learning, whereas Quantitative Analysts focus on financial modeling and trading strategies.

What does a financial data scientist do?

A Financial Data Scientist analyzes complex financial data using statistical, machine learning, and computational techniques to identify patterns, forecast trends, and support decision-making within financial institutions. They work with large datasets from sources like market data, customer transactions, and economic indicators to develop predictive models and data-driven strategies. Their work helps organizations manage risk, optimize portfolios, detect fraud, and gain a competitive edge in the financial sector.

How does a financial data scientist typically collaborate with other departments within a financial organization?

Financial Data Scientists regularly work alongside cross-functional teams, including risk analysts, portfolio managers, and software engineers. They collaborate to develop predictive models, automate data pipelines, and translate complex data insights into actionable business strategies. Effective communication is key, as they must explain technical findings to stakeholders with varying levels of data literacy. This collaborative environment not only fosters innovation but also offers opportunities to learn from other experts and expand your professional network.
What are popular job titles related to Financial Data Scientist jobs in Raleigh, NC? For Financial Data Scientist jobs in Raleigh, NC, the most frequently searched job titles are:
What job categories do people searching Financial Data Scientist jobs in Raleigh, NC look for? The top searched job categories for Financial Data Scientist jobs in Raleigh, NC are:
Financial Data Scientist jobs near you
Infographic showing various Financial Data Scientist job openings in Raleigh, NC as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 13% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $119,312 per year, or $57.4 per hour.

Data Product Architect

NetApp

Morrisville, NC • Hybrid

$59.75 - $76.75/hr

Full-time

Re-posted 23 days ago

NetApp rating

9.4

Company rating: 9.4 out of 10

Based on 5 frontline employees who took The Breakroom Quiz

2nd of 483 rated business services

Job description

Job Summary
NetApp seeks a dynamic and innovative Data Product Architect to join our Enterprise Data & Analytics team. In this position, you will convert raw data into actionable insights that enhance decision-making and improve customer outcomes within Customer Success Operations (CSOps) and Professional Services Operations (PSOps).
You will design scalable data models, build robust data pipelines, and explore cutting-edge technologies such as advanced analytics, data science, machine learning, and AI tools for coding, SQL generation, data modeling, and conversational analytics. Utilizing these technologies, you will create data products that automate workflows and strengthen decision-making. Your work will enable teams to harness data to boost operational efficiency, increase customer engagement, and elevate overall business performance.
At NetApp, we prioritize ownership, a growth mindset, customer focus, inclusivity, and high performance. We encourage applicants from diverse backgrounds and experiences, emphasizing skills and potential above traditional credentials. This hybrid role requires presence at the NetApp RTP office three days per week.
Key Responsibilities
Design and refine data models that effectively support enterprise analytics and reporting requirements. Establish clear data structures, define relationships, and set metadata standards to ensure scalability and optimal performance.
Construct, enhance, and assess data pipelines to produce reliable, production-ready systems. Employ tools such as Oracle Data Warehouse, Snowflake, HVR, and Informatica IICS to streamline workflows.
Harness AI technologies for coding assistance, SQL generation, data modeling, and conversational analytics. Create intelligent data products that automate workflows and improve decision-making processes.
Comprehend business needs within CSOps and PSOps, translating them into precise technical designs and architectural documentation. Align solutions with organizational goals while collaborating with cross-functional teams to deliver effective data-driven outcomes.
Implement Waterfall and Agile/Scrum methodologies throughout project lifecycles, ensuring adherence to compliance standards, especially concerning financial data and operational controls.
Job Requirements
Expertise in SQL, RESTful APIs, and Python, with NoSQL experience considered an advantage.
At least five years of experience working with data platforms such as Oracle Data Warehouse, Snowflake, and Informatica IICS.
A minimum of five years of practical Power BI experience, covering semantic modeling, DAX, dashboard creation, and enterprise-level reporting.
Proficient in Git-based version control and Azure DevOps CI/CD methodologies for efficient code management, deployment, and release processes.
At least two years of experience utilizing AI tools for coding, SQL generation, data modeling, and conversational analytics.
Outstanding written and verbal communication and presentation skills.
Strong analytical thinking and problem-solving capabilities.
Collaborate effectively with stakeholders including Project Managers, Delivery Managers, and Architects on both projects and operational tasks.
Demonstrated ability to work seamlessly within cross-functional teams and to influence stakeholders.
Preferred Certifications:
  • Snowflake: Snowflake SnowPro Core Certification
  • TOGAF: TOGAF 9 Certified or TOGAF Standard, Version 10 Certification
  • Power BI: Microsoft Certified: Power BI Data Analyst Associate
  • Additional relevant industry certifications are highly welcomed.
Education

Bachelor's degree in computer science or management information systems.

Minimum of five years of experience is required; 5 to 8 years of experience is preferred. including three or more years in Business Analytics or relevant domains.

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Hours and flexibility

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