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Computer Science Finance Jobs in Ohio (NOW HIRING)

Bachelor's degree in computer science, Information Systems, or related field. * Deep Finance and Accounting knowledge. * 8-10+ years of experience in ERP implementations, business analysis, or ...

Data Scientist

Columbus, OH · On-site +1

$101K - $198K/yr

Job Summary The Data Science role at Bread Financial delivers best-in-class actionable business ... Bachelor's Degree in Statistics, Mathematics, Engineering, Data Science, Computer Science, or ...

Accounting, Analytics/Data Science, Business Administration/Management, Computer Science/Information Systems, Finance, Risk Management/Insurance - Demonstrating strategic leadership in financial ...

Accounting, Analytics/Data Science, Business Administration/Management, Computer Science/Information Systems, Finance, Risk Management/Insurance - Demonstrating strategic leadership in financial ...

Data Scientist

Columbus, OH · On-site

$101K - $198K/yr

Job Summary The Data Science role at Bread Financial delivers best-in-class actionable business ... Bachelor's Degree in Statistics, Mathematics, Engineering, Data Science, Computer Science, or ...

Bachelor's degree in Finance, Accounting, Management Information Systems, Computer Science, Management or other business related field * 1+ years of relevant experience in the financial industry or ...

... of:​ IT SAP Finance Apprentice Poznań, Poland Your responsibilities: ​ * Primary ... Computer Science. * No experience required. * Language Requirements: Polish B2, English C1, both ...

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Showing results 1-20

Computer Science Finance information

See Ohio salary details

$23.8K

$88.1K

$128.8K

How much do computer science finance jobs pay per year?

As of Jul 3, 2026, the average yearly pay for computer science finance in Ohio is $88,064.00, according to ZipRecruiter salary data. Most workers in this role earn between $71,300.00 and $103,600.00 per year, depending on experience, location, and employer.

How is computer science used in finance?

Computer science in finance involves developing algorithms and software for trading, risk management, and data analysis. Professionals use programming languages like Python and tools such as machine learning models to optimize financial decision-making and automate processes.

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 finance and computer science a good combo?

Computer Science Finance combines technical programming skills with financial knowledge, making it valuable in areas like quantitative analysis, algorithmic trading, and financial modeling. Professionals in this field often use tools like Python, R, and SQL, and benefit from certifications such as CFA or FRM to enhance career prospects.

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

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

Is computer science dead due to AI?

Computer science remains a vital field for roles such as software developers, data scientists, and AI specialists. AI advances create new opportunities for innovation, requiring skills in programming, algorithms, and machine learning tools, ensuring continued demand for computer science 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 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 are popular job titles related to Computer Science Finance jobs in Ohio? For Computer Science Finance jobs in Ohio, the most frequently searched job titles are:
What cities in Ohio are hiring for Computer Science Finance jobs? Cities in Ohio with the most Computer Science Finance job openings:
Infographic showing various Computer Science Finance job openings in Ohio as of June 2026, with employment types broken down into 96% Full Time, 3% Part Time, and 1% Contract. Highlights an 83% Physical, 6% Hybrid, and 11% Remote job distribution, with an average salary of $88,064 per year, or $42.3 per hour.
Applied AI ML Lead - Sales Science

Applied AI ML Lead - Sales Science

JPMorgan Chase & Co

Columbus, OH • On-site

Full-time

Medical, Retirement

Posted 17 days ago


JPMorgan Chase & Co. rating

8.0

Company rating: 8.0 out of 10

Based on 486 frontline employees who took The Breakroom Quiz

54th of 144 rated banks


Job description

Join our Sales Science Data and Analytics team and help us utilize AI and LLM tools to optimize banker and client engagement.
As an Applied AI ML Lead on the Sales Science team, you will contribute to innovative projects and drive the future of field AI Technologies, leveraging ML tools and algorithms to deliver the right solutions as we build interactive coaching tools for the firm.  You will be part of an innovative team, working closely with business partners, product owners, and fellow data scientists to build new AI/ML solutions and productionlize them. We are looking for someone with a passion for data, ML, and programming, who can build ML solutions at-scale with a hands-on approach with detailed technical acumen.
Job responsibilities
 

  • Serve as a subject matter expert on a wide range of ML techniques and optimizations.
     
  • Build and enhance ML workflows through advanced proficiency in large language models (LLMs) and related techniques.
     
  • Conducting experiments using latest ML technologies, analyzing results, tuning models.
     
  • Actively engage in hands-on coding to convert experimental results into robust production solutions.
     
  • Take full ownership of the entire code development lifecycle in Python, from proof of concept and experimentation to delivering production-ready solutions.
     
  • Integrate Generative AI within the ML Platform using state-of-the-art techniques.
     

Required qualifications, capabilities, and skills
 

  • Bachelor's degree with 7 years of applied machine learning experience.
     
  • 5+ years of experience in one of the programming languages like Python, R, Java, etc. Intermediate Python is a must.
     
  • Experience in applying data science, ML techniques to solve business problems.
     
  • Solid background in Natural Language Processing (NLP) and Large Language Models (LLMs)
     
  • Experience with machine learning and deep learning methods.
     
  • Deep understanding and expertise in deep learning frameworks such as PyTorch or TensorFlow
     
  • Ability to work on tasks and projects through to completion with limited supervision.
     
  • Passion for detail and follow through. Excellent communication skills and team player.
     

Preferred qualifications, capabilities, and skills
 

  • In-depth understanding of Search/Ranking, Recommender systems, Graph techniques, and other advanced methodologies.
     
  • MS and/or PhD in Computer Science, Machine Learning, or a related field, with at least 5 years of applied machine learning experience preferred.
     
  • Advanced knowledge in Reinforcement Learning or Meta Learning.
     
  • Software development experience is a plus.
     
  • Demonstrated ability to translate LLM pipelines/workflows into something less technical business partners can understand.
     
  • Deep understanding of Large Language Model (LLM) techniques, including Agents, Planning, Reasoning, and other related methods.
     
  • Experience with building and deploying ML models on cloud platforms such as AWS and AWS tools like Sagemaker, EKS, etc.

Chase is a leading financial services firm, helping nearly half of America's households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs. 

We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions.  We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process. 

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.

Equal Opportunity Employer/Disability/Veterans

Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions - all while ranking first in customer satisfaction.

The CCB Data & Analytics team responsibly leverages data across Chase to build competitive advantages for the businesses while providing value and protection for customers. The team encompasses a variety of disciplines from data governance and strategy to reporting, data science and machine learning. We have a strong partnership with Technology, which provides cutting edge data and analytics infrastructure. The team powers Chase with insights to create the best customer and business outcomes.

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