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Computer Science Finance Jobs in Dallas, TX (NOW HIRING)

Bachelor's degree required in Business Administration, Computer Science, Finance, Accounting, or a quantitative field such as Mathematics, Statistics, Operations Research, Engineering * 2 to 5 years ...

Bachelor's degree in Computer Science, Data Science, Information Systems, Finance, Accounting, Engineering, Mathematics, or Statistics * Ability to travel 50%, on average, based on the work you do ...

Will accept a Bachelor's degree in Mathematics, Computer Science, Statistics, Economics, Finance, Actuarial Sciences, Science and Engineering or related field and 4 years of experience in the job ...

Will accept a Bachelor's degree in Mathematics, Computer Science, Statistics, Economics, Finance, Actuarial Sciences, Science and Engineering or related field and 4 years of experience in the job ...

Bachelor's degree in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative discipline; OR 4 years of experience in ...

... Computer Science) and 5+ years of finance experience, or Master's degree and 3+ years of finance experience PREFERRED QUALIFICATIONS - 6+ years of identifying incomplete or inaccurate data ...

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Computer Science Finance information

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$24.7K

$91.6K

$134K

How much do computer science finance jobs pay per year?

As of Jun 14, 2026, the average yearly pay for computer science finance in Dallas, TX is $91,634.00, according to ZipRecruiter salary data. Most workers in this role earn between $74,200.00 and $107,800.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in Computer Science Finance, and why are they important?

To thrive in Computer Science Finance, you need strong analytical and programming skills, a solid understanding of financial concepts, and typically a degree in computer science, finance, or a related field. Familiarity with financial modeling tools, database management systems, and programming languages like Python, R, or SQL is highly valued, along with certifications such as CFA or FRM. Excellent problem-solving abilities, attention to detail, and effective communication are essential soft skills for collaborating with diverse teams and interpreting complex data. These skills are crucial for developing innovative financial solutions, ensuring data integrity, and driving informed decision-making in the fast-paced finance industry.

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

AspectComputer Science FinanceData Analyst
Required CredentialsBachelor's in Computer Science, Finance, or related fields; certifications like CFA or FRM beneficialBachelor's in Statistics, Economics, or related fields; certifications like CAP or Microsoft Data Analyst
Work EnvironmentFinancial institutions, tech firms, investment banks; often collaborative and fast-pacedCorporate offices, consulting firms, financial services; data-driven and analytical
Employer & Industry UsageFinance, banking, fintech, tech companiesFinance, marketing, healthcare, consulting

Computer Science Finance professionals combine technical skills with financial knowledge to develop algorithms, models, and software for financial analysis and trading. Data Analysts focus on interpreting data to inform business decisions across various industries. While both roles require analytical skills, Computer Science Finance emphasizes programming and financial expertise, whereas Data Analysts concentrate on data interpretation and reporting.

What is computer science finance?

Computer science finance is an interdisciplinary field that combines principles of computer science with finance. Professionals in this area use technology and programming to analyze financial data, develop trading algorithms, manage risk, and optimize investment strategies. Careers in computer science finance often involve roles such as quantitative analyst, financial software developer, or data scientist for investment firms, banks, or fintech companies. This field requires skills in programming (often Python, R, or C++), data analysis, and a solid understanding of financial markets and instruments.

Is CS useful in finance?

Computer Science (CS) is highly useful in finance, as it provides skills in programming, data analysis, and algorithm development that are essential for quantitative analysis, trading algorithms, and financial modeling. Many finance roles require knowledge of programming languages like Python or R, and familiarity with data structures and databases enhances efficiency in managing large datasets. CS expertise can improve decision-making and automate processes in financial services.

Can I get a finance job with a computer science degree?

A computer science degree can qualify you for finance jobs such as quantitative analyst, data analyst, or financial software developer, especially if you have skills in programming, data analysis, and financial modeling. Many finance roles value technical expertise, and additional certifications like CFA or knowledge of tools like Python and SQL can enhance your prospects.

Can I make 200K with a computer science degree?

Computer science professionals can reach a $200,000 salary with experience in high-demand roles such as software engineering, data science, or cybersecurity, especially in senior or specialized positions. Achieving this often requires advanced skills, certifications, and working in competitive industries or locations with high living costs. Salary levels vary based on factors like location, company size, and individual expertise.

How does a professional in Computer Science Finance typically collaborate with both technical and financial teams?

Professionals in Computer Science Finance often serve as a bridge between technology and finance departments, translating financial requirements into technical solutions. They might collaborate closely with software engineers to develop financial models or automation tools, and work with analysts or traders to understand market needs and ensure technical solutions align with business goals. Effective communication is key, as they regularly participate in cross-functional meetings, manage project timelines, and provide updates to both technical and non-technical stakeholders. This role requires adaptability and the ability to explain complex concepts in accessible terms.

What jobs make $1,000,000 a year?

In the field of computer science finance, roles such as senior quantitative analysts, hedge fund managers, and chief technology officers at large financial firms can earn $1,000,000 or more annually. These positions typically require advanced skills in programming, data analysis, and financial modeling, along with extensive experience and often performance-based bonuses or profit sharing.

What Are Finance Jobs for Computer Science Majors?

Finance jobs for computer science majors focus on the analysis of financial data, the development of finance technology (fintech) software and applications to analyze financial markets and automate equities trading, and the creation of algorithms for analysis, fraud detection, and risk management. As a data scientist or quantitative analyst, you perform your duties for an investment firm or bank. If you are a risk management analyst, you work for financial institutions or life insurance companies. A computer science major can also develop software and configure databases for finance businesses or have cybersecurity responsibilities that include protecting data and systems from hackers.

What cities near Dallas, TX are hiring for Computer Science Finance jobs? Cities near Dallas, TX with the most Computer Science Finance job openings:
Infographic showing various Computer Science Finance job openings in Dallas, TX as of June 2026, with employment types broken down into 83% Full Time, and 17% Part Time. Highlights an 82% Physical, 7% Hybrid, and 11% Remote job distribution, with an average salary of $91,634 per year, or $44.1 per hour.
AWM-Marcus by Goldman Sachs, Marketing Analytics, Associate/Analyst Richardson, TX,

AWM-Marcus by Goldman Sachs, Marketing Analytics, Associate/Analyst Richardson, TX,

Goldman Sachs

Richardson, TX

Other

Posted 3 days ago


Goldman Sachs rating

8.3

Company rating: 8.3 out of 10

Based on 25 frontline employees who took The Breakroom Quiz

29th of 141 rated banks


Job description

Opportunity Overview 

As a Marketing Analytics, Associate/Analyst within the Commercial Strategy team, you will sit at the intersection of data science and business growth. You will be responsible for deciphering complex consumer behaviors to optimize marketing spend, improve customer acquisition, and drive the long-term profitability of our digital banking products. This role requires a deep understanding of modern, privacy-centric measurement frameworks and the ability to translate technical findings into commercial strategies. 

Asset & Wealth Management 

The Asset & Wealth Management Division include Goldman Sachs Asset Management (GSAM), Private Wealth Management (PWM) and Marcus Savings business (MS). We provide asset management, wealth management and banking expertise to consumers and institutions around the world. AWM partners with various teams across the firm to help individuals and institutions navigate changing markets and take control of their financial lives. 

Marcus by Goldman Sachs 

As the online consumer banking business of Goldman Sachs, Marcus operates as a digital bank, providing high-yield savings accounts and Certificates of Deposit (CDs) directly to individual consumers. Marcus combines Goldman Sachs' 150+ years of expertise with intuitive digital experiences, focusing on value, transparency, and simplicity for its millions of customers, and is recognized as the largest pure online bank, delivering a fully digital experience without physical branches. 

Responsibilities: 

  • Advanced Marketing Measurement & Attribution:Develop and maintain measurement frameworks to evaluate the effectiveness of multi-channel marketing campaigns (Search, Social, Display, Email, CTV, Audio/Radio). Join first-party customer data with event-level ad data for deep-dive attribution and audience analysis. 
  • Privacy-First Identity Resolution:Utilize data clean rooms(e.g. Liveramp, Snowflake, InfoSum) to perform privacy-safe data collaboration and identity stitching across fragmented touchpoints. 
  • Incrementality & Causal Analysis:Design and executerandomized controlled tests (RCTs) and geo-testing using frameworks likeGeoLiftto measure the true incremental impact of marketing investments. 
  • Marketing Mix Modeling (MMM):ConductMarketing Mix Modeling (MMM)using open-source libraries (e.g., Meta'sRobynor Google'sLightweightMMM) to estimate marketing contribution and inform marketing budget planning and optimization. 
  • Causal Inference:Apply advanced statistical techniques and Python libraries (e.g.,EconML,CausalML) to move beyond correlation and understand the drivers of customer behavior. 
  • Automated Reporting:Build and automate high-impact dashboards (e.g. Tableau, PowerBI)to provide real-time insights into marketing ROI, channel performance, campaign double-clicks/deep-dives, promotion effectiveness , and prospect/customer cohort analysis. 
  • Cross-Functional Collaboration:Work closely with technology and modeling teams to evaluate new data sources and analytical tools, ensuring the marketing stack remains cutting-edge. 
  • Growth Strategy & Optimization:Use data and statistical testing to identify opportunities for improving conversion rates across the customer acquisition funnel and optimizing Customer Acquisition Cost (CAC). 
  • A/B Testing & Experimentation:Design, execute, and monitor "test and learn" strategies for marketing creative, landing pages, and promotional offers, ensuring results meet statistical significance. 
  • Automated Dashboards:Build and automate high-impact visualizations in Tableau to provide real-time insights into marketing ROI and channel performance for senior leadership. 
  • Cross-Functional Collaboration:Partner with the Modeling, Engineering, and Product teams to integrate new data sources and refine the marketing tech stack. 

Qualifications: 

  • Education:Advanced degree (Master's preferred) in a quantitative field such as Statistics, Applied Mathematics, Engineering, Computer Science, Finance or Economics. 
  • Experience:1-2 years of experience in marketing analytics, data science, or commercial strategy, preferably within consumer banking, financial services or a high-growth consumer tech environment. 
  • Technical Skills: 
  • Programming:Expert-levelSQLandPython(specifically for data science and statistics). 
  • Marketing Tech:Hands-on experience withLiveRamp,Google Ads Data Hub, and/orData Clean Rooms. 
  • Measurement Methodologies:Proven track record in conductingMMM,Geo-testing, andCausal Inference. 
  • Visualization:Proficiency inTableauandAdvanced Excel. 
  • Analytical Rigor:Strong understanding of statistical concepts (e.g., regression, hypothesis testing, significance levels). 
  • Communication:Demonstrated ability to present complex analytical results to non-technical stakeholders, highlighting commercial and strategic impacts. 
  • Project Management:Self-driven with the ability to manage multiple workstreams independently in a fast-paced environment. 

Technical Competency Requirements 

Specific Tools & Frameworks 

Identity & Privacy 

LiveRamp, Snowflake Data Clean Rooms, InfoSum 

Measurement Platforms 

Google Ads Data Hub (ADH), Amazon Marketing Cloud (AMC) 

Advanced Analytics 

Meta Robyn (MMM),GeoLift 

Causal Inference 

Microsoft EconML, CausalML, Propensity Score Matching 

Data Stack 

SQL, Python, Snowflake, Alteryx, BigQuery, Tableau/ PowerBI 

ABOUT GOLDMAN SACHS 

At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world. 

We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. Learn more about our culture, benefits, and people at GS.com/careers. 

We're committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more:https://www.goldmansachs.com/careers/footer/disability-statement.html 

The Goldman Sachs Group, Inc., 2026. All rights reserved. 

Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veterans status, disability, or any other characteristic protected by applicable law. 


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About Goldman Sachs

Sourced by ZipRecruiter

At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world. We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs.

Industry

Finance and insurance

Company size

10,000+ Employees

Headquarters location

New York, NY, US

Year founded

1869