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Bank Python Jobs in Orem, UT (NOW HIRING)

Sr. Analyst Flow Lending

South Jordan, UT

$81.60K - $101.70K/yr

The bank that builds * CardWorks Servicing: One partner, total performance * Carson Smithfield ... Proficiency with SQL or a scripting language (e.g., Python, R). * Comfort working hands-on with ...

In this role, you will have a direct impact on the bank's core infrastructure by ensuring high ... Develop solutions and system integrations using Python, Java, and RESTful APIs. * Utilize ...

Senior Data Analyst - Remote

Draper, UT · On-site +1

$80.40K - $101.40K/yr

Leverage Python (e.g., pandas, NumPy) to perform advanced data analysis, automation, validation ... Ensure adherence to data governance, security, and regulatory requirements specific to banking and ...

... banking processes, and transform data into actionable insights. You will work alongside other ... Languages: Python, R, or Java * Libraries/Frameworks: NumPy, Scikit-learn, PyTorch, TensorFlow

... banking processes, and transform data into actionable insights. You will work alongside other ... Languages: Python, R, or Java * Libraries/Frameworks: NumPy, Scikit-learn, PyTorch, TensorFlow

Familiarity with Java, SQL, Python, JavaScript. * Experience with DevSecOps, CI/CD, build/deploy ... Employee Ambassador preferred banking products

Senior Data Scientist

Lehi, UT

$107.90K - $183.40K/yr

Do you want to make a significant impact on complex banking problems and be a driving force in the ... Demonstrated mastery of programming skills in Python or R * Experience with data visualization ...

Senior Data Scientist

Lehi, UT · On-site

$107.90K - $183.40K/yr

Do you want to make a significant impact on complex banking problems and be a driving force in the ... Demonstrated mastery of programming skills in Python or R * Experience with data visualization ...

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Bank Python information

See Orem, UT salary details

$11

$50

$75

How much do bank python jobs pay per hour?

As of May 31, 2026, the average hourly pay for bank python in Orem, UT is $50.96, according to ZipRecruiter salary data. Most workers in this role earn between $42.02 and $57.88 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Bank Python Developer, and why are they important?

To thrive as a Bank Python Developer, you need strong programming skills in Python, expertise in financial data analysis, and a relevant degree in computer science or a related field. Familiarity with SQL databases, version control systems like Git, and experience with financial software or regulatory compliance tools are typically required. Attention to detail, problem-solving abilities, and effective communication are important soft skills that help in collaborating with cross-functional teams and ensuring accuracy. These skills and qualities are crucial to efficiently develop reliable banking applications, maintain system integrity, and adapt to the fast-evolving financial technology landscape.

How do Python developers in banking typically collaborate with other teams, such as risk management or data analysis?

Python developers in banking frequently work in cross-functional teams, collaborating closely with professionals from risk management, data analysis, and operations. They often translate complex business requirements into efficient, automated solutions, such as risk assessment models or data pipelines. Regular meetings, code reviews, and joint project planning are common practices to ensure alignment and compliance with regulatory standards. This collaborative environment not only enhances the quality of deliverables but also provides developers with a broader understanding of banking operations.

What is a Bank Python?

A Bank Python is not a recognized job title within the banking or technology industries. It may refer to a Python developer working in a bank, responsible for building, maintaining, or automating financial applications using the Python programming language. These professionals help streamline banking operations, develop algorithms for financial analysis, and ensure secure data processing. Their work is crucial for enhancing efficiency and accuracy in banking systems.

What is the difference between Bank Python vs Bank Data Analyst?

AspectBank PythonBank Data Analyst
Required CredentialsPython programming skills, possibly certifications in data analysis or programmingDegree in finance, economics, or data analysis; certifications like CFA or data analytics certifications
Work EnvironmentTech-focused teams within banks, working on automation, data processing, and software developmentFinancial institutions, analyzing data to inform business decisions, reporting, and risk assessment
Industry UsageUsed for automating banking processes, developing financial models, and data managementUsed for interpreting financial data, creating reports, and supporting strategic decisions

Bank Python professionals focus on programming and automation within banking systems, while Bank Data Analysts interpret financial data to guide business strategies. Both roles are vital in modern banking but differ in skill sets and daily tasks.

What are popular job titles related to Bank Python jobs in Orem, UT? For Bank Python jobs in Orem, UT, the most frequently searched job titles are:
AI & Process Automation Engineer

AI & Process Automation Engineer

CCBank

Pleasant Grove, UT

Full-time

Posted 21 days ago


Job description

Summary

Capital Community Bank is seeking a highly motivated AI & Process Automation Engineer to serve as the Bank's first dedicated AI resource. This role is responsible for identifying, designing, building, and deploying AI-driven solutions and lightweight software tools that improve operational efficiency across the organization.

The position will embed directly with business teams, starting in Compliance, then rotating through Operations, Lending, Risk, Finance, and other areas, to discover automation opportunities and deliver measurable results. This role bridges business units, IT, and compliance to ensure AI is implemented responsibly, securely, and effectively.

Key Responsibilities

AI Opportunity Identification & Development

  • Partner with business units across the Bank to identify high-impact problems that can be solved using AI, automation, and intelligent tools.
  • Translate business needs into structured Project Charters that define estimated value, delivery timelines, and measurable success criteria.
  • Design, build, and deploy AI-powered solutions, including process automations, data extraction tools, and workflow optimizations, using both custom code, AI coding tools and existing platforms.
  • Develop lightweight, user-friendly software interfaces that enable non-technical staff to adopt and effectively use deployed solutions.
  • Continuously evaluate and improve deployed solutions based on performance data and user feedback.

AI Governance & Risk Management

  • Maintain an inventory of AI tools, use cases, and data access points across the organization.
  • Ensure all AI implementations comply with internal policies, banking regulations, data privacy requirements, and the Bank's risk management framework.
  • Collaborate with IT, Security, and Compliance to assess risks and approve new AI use cases prior to deployment.
  • Document data flows, system interactions, and AI decision-making processes for audit and regulatory readiness.

Documentation & Process Standardization

  • Create and maintain comprehensive documentation for AI workflows, configurations, and business processes.
  • Develop SOPs, training materials, and user guides for all deployed AI tools.
  • Establish repeatable, scalable processes and frameworks for future AI implementations.

Training & Adoption

  • Provide hands-on training and support to end users on AI tools and best practices.
  • Serve as the Bank's internal subject matter expert on AI, automation, and emerging technology.
  • Drive adoption through education, workshops, and ongoing support; help build an innovation-forward culture.

Performance Tracking & Reporting

  • Define and track KPIs to measure the impact of AI initiatives, including time savings, cost reduction, efficiency gains, and adoption rates.
  • Report on AI usage, adoption, and ROI to senior leadership on a regular cadence.
  • Continuously identify opportunities for improvement, expansion, and new project pipeline development.

Qualifications

Required:

  • Advanced degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a closely related field, preferred.
  • Strong programming skills in Python and experience with AI/ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn, or similar).
  • Ability to design and build functional, user-facing software applications (web or desktop) with clean, intuitive interfaces.
  • Experience with generative AI tools and APIs (e.g., OpenAI, Anthropic, open-source LLMs).
  • Strong documentation and process design skills.
  • Understanding of data sensitivity, security, and compliance principles.
  • Excellent communication skills with the ability to translate complex technical concepts into clear, business-friendly language.
  • Self-directed work style with the ability to manage multiple projects and stakeholders simultaneously.
  • Must pass a background check, credit check, and drug screen.

Preferred:

  • Experience in banking, financial services, fintech, or other regulated industries.
  • Familiarity with front-end development frameworks (React, Vue, or similar) for building lightweight internal tools.
  • Working knowledge of SQL, databases, and data pipeline design.
  • Understanding of compliance and regulatory technology concepts (BSA/AML, KYC, TILA, UDAAP, FFIEC guidelines).
  • Familiarity with data governance, risk management, or compliance frameworks.
  • Experience deploying solutions in cloud environments (AWS, Azure, or GCP).
  • Experience with workflow automation tools or scripting (Power Automate, Zapier, etc.).

Key Success Metrics

  • Number and quality of AI solutions successfully deployed and adopted.
  • Time and cost savings generated by deployed tools, as documented in Project Charters.
  • AI adoption rates across departments.
  • Compliance adherence, zero data incidents related to AI usage.
  • Documentation completeness and audit readiness.
  • Stakeholder satisfaction with delivered solutions.