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Computer Science Banking Jobs in Georgia (NOW HIRING)

US Bank

AI Research Scientist

Atlanta, GA · On-site

Bank, we're on a journey to do our best. Helping the customers and businesses we serve to make ... Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Applied Mathematics ...

US Bank

Principal AI Research Scientist

Atlanta, GA · On-site

Bank, we're on a journey to do our best. Helping the customers and businesses we serve to make ... D. in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Applied ...

Truist

Data Scientist II

Atlanta, GA · On-site

... Science, Computer Science, or Engineering 2. Four years of relevant work experience if candidate lacks graduate degree 3. Previous experience in the banking or fin-tech industry OTHER JOB ...

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All JobsComputer Science Banking JobsComputer Science Banking Jobs in Georgia

Computer Science Banking information

See Georgia salary details

$15.9K

$52.8K

$110.1K

How much do computer science banking jobs pay per year?

As of Aug 6, 2026, the average yearly pay for computer science banking in Georgia is $52,818.00, according to ZipRecruiter salary data. Most workers in this role earn between $31,511.00 and $73,137.00 per year, depending on experience, location, and employer.

What types of projects do professionals in computer science banking typically work on?

Professionals in Computer Science Banking often work on projects such as developing secure online banking platforms, implementing fraud detection systems, optimizing transaction processes, and managing large financial data sets. They may also support regulatory compliance projects and enhance cybersecurity measures to protect sensitive customer information. Collaboration with financial analysts, management, and IT teams is common, as is involvement in implementing emerging technologies like artificial intelligence or blockchain. This dynamic environment provides valuable experience across technical and financial domains and supports career advancement into senior technical or leadership roles.

How is computer science used in banking?

Computer science in banking involves developing and maintaining software systems for transactions, fraud detection, risk management, and customer service. Professionals in this field use programming languages, data analysis, and cybersecurity skills to improve efficiency and security within financial institutions.

What are the top 3 jobs in computer science?

In computer science, the top jobs include software developer, data scientist, and cybersecurity analyst. These roles typically require strong programming skills, knowledge of algorithms, and familiarity with tools like Python, Java, or security frameworks. They are in high demand across various industries due to increasing reliance on technology and data security.

What is a computer science banking?

A Computer Science Banking job involves applying computer science principles to develop, maintain, and secure banking systems. Professionals in this field work on digital banking platforms, cybersecurity, data analytics, and financial software development. They help banks enhance customer experience, prevent fraud, and optimize operations through technology. Roles may include software developers, cybersecurity analysts, data scientists, and IT specialists in financial institutions. These jobs require expertise in programming, databases, encryption, and financial technologies.

What are the key skills and qualifications needed to thrive in computer science banking, and why are they important?

To excel in a Computer Science Banking role, you need a strong background in computer science principles, programming, and data analysis, ideally supported by a relevant degree or certifications. Familiarity with banking software platforms, cybersecurity protocols, databases, and tools like Python, SQL, or Java are often required. Strong problem-solving skills, attention to detail, and clear communication help you work effectively with both technical teams and finance professionals. These competencies ensure secure, efficient, and innovative technology solutions within the fast-paced and highly regulated banking sector.

What are popular job titles related to Computer Science Banking jobs in Georgia? For Computer Science Banking jobs in Georgia, the most frequently searched job titles are:
What job categories do people searching Computer Science Banking jobs in Georgia look for? The top searched job categories for Computer Science Banking jobs in Georgia are:
Computer Science Banking jobs near you
Infographic showing various Computer Science Banking job openings in Georgia as of August 2026, with employment types broken down into 84% Full Time, 5% Part Time, and 11% Contract. Highlights an 92% In-person, 5% Hybrid, and 3% Remote job distribution, with an average salary of $52,818 per year, or $25.4 per hour.

Data Scientist with Banking & Finance Experience

Cortex consultants LLC

Atlanta, GA • On-site

Contractor

Re-posted 26 days ago

Job description

Title: Data Scientist with Banking & Finance Experience
Location: Atlanta, GA
Work Mode: Hybrid ( 3 days from office) (T, W, T)
Over all Years of experience: Min 12+Years
Must have:
  • Python
  • Jupyter
  • TensorFlow
  • Keras
  • ScikitLearn
Experience needed
  • BS degree in a STEM major or equivalent discipline
  • 7-8 years of experience in a related analytical role
  • Overall 12+ Years of experience
  • Proven track record of designing and developing predictive models in real-world applications, preferably Banking and Finance domain
  • Experience with model performance evaluation and predictive model optimization for accuracy and efficiency
Good to have experience
  • Cloud certification strongly preferred
  • Additional role-based certifications may be required depending upon region/BU requirement
  • Passion for data science, data mining, machine learning and experience with big data architectures and methods
  • Past Experience in SAS or Operations Research models
Roles and Responsibility
  • Entity Resolution and Innovation, manipulate large data sets,
  • Integrate diverse data sources, data types and data structures into solutions under management guidance.
  • Analytical approaches to meet business requirements and
  • explore integrating AI methodologies within Entity Resolution System, which involves translating requests into use cases, test cases, preparation of training data sets and iterative algorithm development.
  • Qualification
  • Proven track record of designing and developing predictive models in real-world applications
  • Experience with model performance evaluation and predictive model optimization for accuracy and efficiency
  • Additional role-based certifications may be required depending upon region/BU requirements
  • Passion for data science, data mining, machine learning and experience with big data architectures and methods
  • A master's degree in a quantitative field (Statistics, Mathematics, Economics)
Responsibilities
  • Utilize combined knowledge of data structures, analytics, algorithms/models, and strong computer science fundamentals to independently prepare datasets, conduct analytics, and develop deployable solutions
  • Collect, analyze and interpret large data assets to define and build multiple innovative solution components leveraging business and technical expertise
  • Support the analytical strategy by understanding critical technical capabilities and suggesting opportunities
  • Lead the development or projects with multiple deliverables, leveraging business and technical expertise
  • Work on high-complexity tasks in problems often within multiple business or analytical domains, collaborating with other teams to develop predictive models, risk assessments, fraud detection, recommendation engines, etc. encouraging enhanced solutions
  • Package, summarize, visualize and perform storytelling on analytical findings and results for management and business users
  • Communicate results to external stakeholders and mid level leadership, able to communicate business impact of work
  • Evaluate the technical work of peer and junior data scientists, guiding them on deliverable quality and accuracy