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Fraud Detection Machine Learning Jobs in Utah (NOW HIRING)

Varsity Tutors

Machine Learning Tutor

Provo, UT · Remote

$40/hr

Emphasizes practical model development workflow and connects machine learning to recommendation systems, fraud detection, and predictive analytics. * Curriculum Awareness & Adaptive Instruction:

Varsity Tutors

Machine Learning Tutor

Logan, UT · Remote

$40/hr

Emphasizes practical model development workflow and connects machine learning to recommendation systems, fraud detection, and predictive analytics. * Curriculum Awareness & Adaptive Instruction:

Varsity Tutors

Machine Learning Tutor

Spanish Fork, UT · Remote

$40/hr

Emphasizes practical model development workflow and connects machine learning to recommendation systems, fraud detection, and predictive analytics. * Curriculum Awareness & Adaptive Instruction:

Varsity Tutors

Machine Learning Tutor

Cedar City, UT · Remote

$40/hr

Emphasizes practical model development workflow and connects machine learning to recommendation systems, fraud detection, and predictive analytics. * Curriculum Awareness & Adaptive Instruction:

SoFi

Fraud Model Analyst

Cottonwood Heights, UT

Familiarity with machine learning concepts and ability to interpret model outputs and performance tradeoffs * Prior experience working with vendor models (e.g., identity, device, or fraud risk ...

SoFi

Fraud Model Analyst

Cottonwood Heights, UT · On-site

Familiarity with machine learning concepts and ability to interpret model outputs and performance tradeoffs * Prior experience working with vendor models (e.g., identity, device, or fraud risk ...

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Fraud Detection Machine Learning information

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How much do fraud detection machine learning jobs pay per hour?

As of Jun 13, 2026, the average hourly pay for fraud detection machine learning in Utah is $16.43, according to ZipRecruiter salary data. Most workers in this role earn between $13.56 and $17.50 per hour, depending on experience, location, and employer.

What are some common challenges faced by professionals working in Fraud Detection Machine Learning, and how can they be addressed?

Professionals in Fraud Detection Machine Learning often face challenges such as dealing with highly imbalanced datasets, rapidly evolving fraud patterns, and the need for real-time detection. Managing data imbalance requires careful selection of evaluation metrics and specialized algorithms. Staying ahead of new fraud tactics involves continuous model retraining and close collaboration with domain experts. Additionally, integrating machine learning solutions with existing systems often requires cross-functional teamwork with IT, security, and compliance teams.

What is fraud detection using machine learning?

Fraud detection using machine learning involves leveraging algorithms and data analysis techniques to identify suspicious or fraudulent activities in various domains, such as banking, e-commerce, or insurance. These systems analyze large volumes of transaction data to detect patterns or anomalies that may indicate fraud. Machine learning models can adapt over time, improving their accuracy as they are exposed to more data. This approach helps organizations automate and enhance their ability to prevent, detect, and respond to fraudulent behavior efficiently.

What is the difference between Fraud Detection Machine Learning vs Fraud Analyst?

AspectFraud Detection Machine LearningFraud Analyst
CredentialsData science, machine learning certifications, programming skillsFinance, criminal justice degrees, analytical skills
Work EnvironmentData-driven, tech-focused, often in financial or e-commerce sectorsInvestigative, report-focused, in financial institutions or insurance companies
Employer & IndustryTech companies, banks, e-commerce platformsFinancial institutions, insurance firms, retail

Fraud Detection Machine Learning involves developing algorithms to identify fraudulent activities automatically, relying heavily on data analysis and programming. Fraud Analysts manually investigate suspicious cases and interpret data insights. While both roles aim to prevent fraud, Machine Learning specialists focus on building models, whereas Fraud Analysts focus on case investigation and decision-making.

What are the key skills and qualifications needed to thrive as a Fraud Detection Machine Learning Specialist, and why are they important?

To thrive as a Fraud Detection Machine Learning Specialist, you need strong expertise in machine learning, statistical analysis, and programming languages like Python or R, typically supported by a degree in computer science, data science, or a related field. Familiarity with tools such as TensorFlow, Scikit-learn, SQL databases, and experience with big data platforms or cloud services is highly valuable. Critical thinking, attention to detail, and effective communication are crucial soft skills for identifying complex fraud patterns and collaborating with interdisciplinary teams. These competencies are vital for developing accurate models that protect organizations from financial losses and maintain trust with customers.
What are popular job titles related to Fraud Detection Machine Learning jobs in Utah? For Fraud Detection Machine Learning jobs in Utah, the most frequently searched job titles are:
What job categories do people searching Fraud Detection Machine Learning jobs in Utah look for? The top searched job categories for Fraud Detection Machine Learning jobs in Utah are:
What cities in Utah are hiring for Fraud Detection Machine Learning jobs? Cities in Utah with the most Fraud Detection Machine Learning job openings:
Fraud Detection Machine Learning jobs near you
Fraud Operations Business Analyst

Fraud Operations Business Analyst

America First Credit Union

Ogden, UT

Full-time

Posted 9 days ago

America First Credit Union rating

8.1

Company rating: 8.1 out of 10

Based on 51 frontline employees who took The Breakroom Quiz

58th of 138 rated financial services

Job description

Mon - Fri Flex

In office and Work @ Home available. Must live in one of the following states: AZ, CA, HI, ID, IA, MI, NV, NH, NJ, NM, OR, TN, TX, UT, VT, VA, & WA.

This role blends analytical expertise with strategic leadership, supporting enterprise-wide fraud initiatives and guiding an Agile Fraud Solutions team to deliver innovative, compliant, and effective fraud mitigation capabilities. This position understands the regulatory landscape, can translate complex compliance requirements into actionable business and technical solutions, and thrives in a collaborative, fast-paced environment.


Fraud & BSA Compliance Analysis

  • Interpret and apply BSA/AML, fraud, and regulatory requirements to business processes, system enhancements, and operational workflows.
  • Conduct risk assessments to identify fraud vulnerabilities and recommend mitigation strategies.
  • Analyze fraud trends, suspicious activity patterns, and operational data to Support decision-making and compliance reporting.
  • Partner with BSA Compliance and Fraud Operations teams to ensure alignment with regulatory expectations and internal policies.
  • Support audits, regulatory exams, and internal reviews by preparing documentation, data, and process explanations.

Business Analysis & Solution Design

  • Gather, document, and refine business requirements for fraud detection systems, case management tools, and compliance platforms.
  • Translate business needs into functional specifications for technology teams.
  • Evaluate current-state processes and recommend improvements that enhance fraud prevention, customer experience, and operational efficiency.
  • Develop user stories, acceptance criteria, and process flows that support Agile
  • Facilitate cross-functional workshops, requirements sessions, and solution design discussions.

Leadership of the Fraud Solutions Agile Team

  • Serve as a support to the Product Owner acting as a Lead Analyst for the Fraud Solutions Agile team, guiding sprint planning, backlog prioritization, and roadmap
  • Ensure the team delivers high-quality fraud prevention capabilities, enhancements, and system integrations.
  • Collaborate closely with developers, QA analysts, data scientists, and business stakeholders to ensure clarity of requirements and alignment with strategic goals.
  • Monitor sprint progress, remove blockers, and ensure timely delivery of features that support fraud detection, case management, and compliance workflows.
  • Champion Agile best practices and foster a culture of continuous improvement within the team.

Stakeholder Engagement & Communication

  • Act as a liaison between Compliance, Fraud Operations, Technology, and business units.
  • Communicate complex fraud and compliance concepts in clear, actionable terms for both technical and non-technical audiences.
  • Prepare executive-level reporting on fraud trends, project progress, and risk
  • Build strong relationships with internal partners to ensure alignment on priorities and outcomes.

#IND21


Required Qualifications:

 

  • Bachelor’s degree in Business, Finance, Information Systems, or related field;
  • 3–7+ years of experience in Fraud, BSA/AML compliance, or financial crimes risk management.
  • Proven experience as a Business Analyst supporting fraud or compliance-related systems.
  • Strong understanding of fraud detection tools, transaction monitoring systems, and case management platforms.
  • Experience working in Agile environments
  • Excellent analytical, problem-solving, and communication skills.
  • Ability to manage competing priorities and drive results in a dynamic environment.

 

Preferred Qualification:

  • Familiarity with machine learning–based fraud models or rules-based detection systems.
  • Experience with Jira, Confluence, or similar Agile tools.
  • Experience with Python and R
  • Strong SQL abilities
  • Experience with both Oracle and Microsoft
  • Knowledge of banking operations, payments, digital channels, or financial services technology.
  • Ability to translate regulatory requirements into technical specifications

What America First Credit Union employees say

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