ZipRecruiter

Log In

1

Software Engineer Fraud Detection Jobs in Arizona

Phoenix Staff

Lead Data & AI Engineer

Phoenix, AZ · On-site +1

$50 - $60/hr

... fraud detection, quality measurement, and care gap analysis. · Operationalize models with strong MLOps practices including versioning, CI/CD, monitoring, and drift detection. · Implement data ...

Hadrian Automation

Robotics Software Engineer

Mesa, AZ · On-site

$160K - $260K/yr

Develop and deploy algorithms for calibration, path optimization, object detection, and collision ... Engineering, or a related field with at least 5 years of developing software for industrial or ...

Rocket Lab Corporation

Senior Software Engineer I

Tucson, AZ · On-site

$115K - $152K/yr

SENIOR SOFTWARE ENGINEER I Rocket Lab's Optical Systems division solves mission-critical space ... Understanding of computer vision approaches, such as object detection, image segmentation, re ...

W. R. Berkley

SIU Manager

Scottsdale, AZ · On-site

Leverage data analytics, predictive modeling, and fraud detection software to enhance investigative capabilities. * Evaluate and implement new tools and technologies to improve SIU efficiency and ...

Intel

AI Software Development Engineer

Phoenix, AZ

... detection. Provide actionable operational dashboards for quality, reliability, data health, and ... Master's degree in software engineering, Computer Engineering, Information Technology, or related ...

next page

Showing results 1-20

All JobsSoftware Engineer Fraud Detection JobsSoftware Engineer Fraud Detection Jobs in Arizona

Software Engineer Fraud Detection information

What does a Software Engineer in Fraud Detection do?

A Software Engineer in Fraud Detection designs and develops systems to identify and prevent fraudulent activities within digital platforms, such as banking or e-commerce environments. They build algorithms to analyze user behavior, detect anomalies, and flag suspicious transactions in real time. Their work often involves machine learning, big data analysis, and close collaboration with data scientists and security teams to continuously improve fraud detection accuracy. These engineers play a key role in protecting businesses and customers from financial loss and cybercrime.

What is the difference between Software Engineer Fraud Detection vs Data Scientist Fraud Detection?

AspectSoftware Engineer Fraud DetectionData Scientist Fraud Detection
Required CredentialsBachelor's in CS or related field, programming skillsBachelor's or higher in CS, Statistics, or Data Science
Work EnvironmentDevelops fraud detection systems, writes code, implements algorithmsAnalyzes data, builds models, interprets results
Employer & Industry UsageFinancial institutions, fintech, e-commerceFinancial services, tech companies, insurance
Common Search & ComparisonFocuses on software development for fraud detectionFocuses on data analysis and modeling for fraud detection

While both roles work in fraud detection, Software Engineer Fraud Detection primarily develops and maintains detection systems through coding, whereas Data Scientist Fraud Detection analyzes data and builds models to identify fraudulent activity. Both roles often collaborate but differ in their core focus and skill sets.

What are the key skills and qualifications needed to thrive as a Software Engineer in Fraud Detection, and why are they important?

To thrive as a Software Engineer in Fraud Detection, strong programming skills (such as Python, Java, or Scala), a solid understanding of algorithms, data structures, and experience with machine learning or statistical analysis are generally required, often supported by a degree in computer science or a related field. Familiarity with big data platforms (like Hadoop or Spark), real-time analytics systems, and fraud detection tools or frameworks is typically expected. Analytical thinking, problem-solving abilities, and effective communication are key soft skills that differentiate top performers in this field. These skills are crucial for developing robust systems that can quickly identify and prevent fraudulent activities, protecting both users and organizations.

How does a Software Engineer in Fraud Detection typically collaborate with data scientists and analysts to identify fraudulent activity?

Software Engineers in Fraud Detection work closely with data scientists and analysts to build, refine, and deploy systems that detect and prevent fraud. While data scientists may develop models and identify patterns from large datasets, engineers are responsible for integrating these models into scalable, real-time systems within the company's technology stack. Regular communication and joint problem-solving are essential, as engineers must understand the logic behind models and analysts' findings to ensure accurate implementation and continuous improvement. This collaborative environment helps create robust fraud detection mechanisms that adapt to evolving threats.
What are popular job titles related to Software Engineer Fraud Detection jobs in Arizona? For Software Engineer Fraud Detection jobs in Arizona, the most frequently searched job titles are:
What job categories do people searching Software Engineer Fraud Detection jobs in Arizona look for? The top searched job categories for Software Engineer Fraud Detection jobs in Arizona are:
What cities in Arizona are hiring for Software Engineer Fraud Detection jobs? Cities in Arizona with the most Software Engineer Fraud Detection job openings:
Software Engineer Fraud Detection jobs near you
Lead Data & AI Engineer

Lead Data & AI Engineer

Phoenix Staff

Phoenix, AZ • On-site, Remote

$50 - $60/hr

Contractor

Re-posted 14 days ago

Job description

Title: Lead Data & AI Engineer

Location: Phoenix, AZ (hybrid remote)

Type: 6-month contract to hire

Pay: $50-60/hr

We’re looking for a Lead Data & AI Engineer to lead the design and delivery of secure, scalable data and AI solutions within complex healthcare environments. The position focuses on building modern data platforms, integrating diverse clinical and claims datasets, and operationalizing machine learning models that improve cost, quality, and patient outcomes.

Your role

·       Design, implement, and optimize data platforms using Snowflake and Microsoft Fabric, including Lakehouses, Warehouses, OneLake, and engineering pipelines.

·       Build and maintain scalable ingestion frameworks for batch and streaming data sources such as APIs, ADLS, SFTP, and event streams with full lineage and governance.

·       Develop secure data environments that comply with HIPAA and PHI requirements using role-based access, masking, tokenization, and de-identification.

·       Create conceptual, logical, and physical data models using dimensional, normalized, and data vault approaches.

·       Transform and normalize structured and unstructured healthcare data including claims, eligibility, enrollment, provider, and clinical documentation.

·       Integrate and harmonize data using FHIR, HL7, X12/EDI 837/835, NCPDP, and CMS standards across payer, provider, EHR, and HIE systems.

·       Build and deploy machine learning pipelines for risk modeling, utilization forecasting, fraud detection, quality measurement, and care gap analysis.

·       Operationalize models with strong MLOps practices including versioning, CI/CD, monitoring, and drift detection.

·       Implement data cataloging, metadata management, lineage tracking, and quality validation using tools such as Microsoft Purview or equivalent.

·       Monitor and optimize pipeline performance, cost, and reliability across Snowflake and Fabric environments.

·       Collaborate with clinicians, actuaries, product teams, and analysts to translate business needs into scalable technical solutions.

·       Document architecture, data mappings, and design standards while mentoring engineers and contributing to enterprise best practices.

What you’ve got

·       8+ years of experience in data engineering or analytics with at least 5 years of hands-on Snowflake expertise including virtual warehouses, tasks, streams, Snowpipe, RBAC, masking, and data sharing.

·       2+ years of experience with Microsoft Fabric including OneLake, Lakehouses, Warehouses, Dataflows Gen2, Notebooks, and Pipelines.

·       Advanced SQL skills with strong experience in ETL/ELT development using Python, dbt, Dataflows, or Fabric/ADF pipelines.

·       Deep knowledge of healthcare data standards including CMS datasets, FHIR, HL7, X12/EDI, provider data, eligibility, and claims processing.

·       Strong data modeling experience including dimensional modeling, SCD types, surrogate keys, 3NF, and data vault methodologies.

·       Experience building and deploying machine learning solutions using tools such as scikit-learn, PyTorch, TensorFlow, Azure ML, or Fabric ML.

·       Practical experience managing HIPAA compliance, PHI handling, auditing, and secure access controls within cloud data environments.

·       Experience working with both structured data formats such as Parquet and CSV and unstructured data such as clinical notes and PDFs.

·       Strong communication skills with the ability to produce mapping specifications, lineage documentation, and present technical trade-offs clearly.

·       Preferred: Experience with Epic or Cerner integrations, HEDIS or risk adjustment programs, MLOps tools such as MLflow or GitHub Actions, Power BI semantic modeling, and relevant Snowflake or Microsoft certifications.

To find more great tech-centric jobs, please visit www.phoenixstaff.com.