ZipRecruiter

Log In

1

Fraud Detection Machine Learning Jobs in Milpitas, CA

DataVisor

Software Engineer

Mountain View, CA

Join us to help usher in more innovative solutions to the fraud detection space. What you'll do: * Design and build machine learning systems that process data sets from the world's largest consumer ...

Poshmark

Sr. Engineer, Machine Learning

Redwood City, CA · On-site

$120 - $150/hr

Our goal is to democratize data science and machine learning, support exploding business, and use machine learning to drive value across the chain (Search, personalization, fraud detection, catalog ...

Unchain Data

Senior Data Scientist, Fraud

Menlo Park, CA · On-site

$187 - $220/hr

We leverage machine learning and analytics to combat malicious behavior in real time, supporting a ... Design and deploy fraud detection models to protect Robinhood users and assets in real time

Intuit

Staff Fraud Risk Analyst

Mountain View, CA · On-site

$176K - $238K/yr

Leverage data driven and machine learning-enabled approaches to devise fraud defenses and rapidly ... Leverage AI and automation to improve fraud detection speed, scalability, and precision ...

Intuit

Staff Fraud Risk Analyst

Mountain View, CA · On-site

$176K - $238K/yr

Leverage data driven and machine learning-enabled approaches to devise fraud defenses and rapidly ... Leverage AI and automation to improve fraud detection speed, scalability, and precision ...

Intuit

Staff Fraud Risk Analyst

Mountain View, CA · On-site

$176K - $238K/yr

Leverage data driven and machine learning-enabled approaches to devise fraud defenses and rapidly ... Leverage AI and automation to improve fraud detection speed, scalability, and precision ...

DataVisor

Data Scientist, AI Solutions

Mountain View, CA · On-site

... fraud patterns into scalable, automated defenses. Responsibilities * Develop Pre-Built Detection Models: Design, back-test, and optimize statistical baselines and machine learning strategies for our ...

DataVisor

Data Scientist, AI Solutions

Mountain View, CA · On-site

... fraud patterns into scalable, automated defenses. Responsibilities * Develop Pre-Built Detection Models: Design, back-test, and optimize statistical baselines and machine learning strategies for our ...

OKX

Sr Data Scientist, Risk Strategy

San Jose, CA · On-site

The Sr Data Scientist, Risk will leverage analytical and modeling skills to identify fraud patterns, collaborate with stakeholders, and develop machine learning models to enhance fraud detection ...

TigerGraph

Account Executive

Milpitas, CA · On-site

$150K - $160K/yr

Its proven technology supports fraud detection, customer 360, MDM, IoT, AI, and machine learning. Fortune 500 organizations and the most innovative mid-size and startup companies choose TigerGraph to ...

TigerGraph

Account Executive

Milpitas, CA · On-site

$150K - $160K/yr

Its proven technology supports fraud detection, customer 360, MDM, IoT, AI, and machine learning. Fortune 500 organizations and the most innovative mid-size and startup companies choose TigerGraph to ...

previous pagenext page

Showing results 21-40

All JobsFraud Detection Machine Learning JobsFraud Detection Machine Learning Jobs in Milpitas, CA

Fraud Detection Machine Learning information

See Milpitas, CA salary details

$12

$21

$31

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

As of Aug 22, 2026, the average hourly pay for fraud detection machine learning in Milpitas, CA is $21.04, according to ZipRecruiter salary data. Most workers in this role earn between $17.36 and $22.40 per hour, depending on experience, location, and employer.

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 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 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 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 popular job titles related to Fraud Detection Machine Learning jobs in Milpitas, CA?

For Fraud Detection Machine Learning jobs in Milpitas, CA, the most frequently searched job titles are:

What cities near Milpitas, CA are hiring for Fraud Detection Machine Learning jobs?

Cities near Milpitas, CA with the most Fraud Detection Machine Learning job openings:

Fraud Detection Machine Learning jobs near you
Infographic showing various Fraud Detection Machine Learning job openings in Milpitas, CA as of August 2026, with employment types broken down into 100% Full Time. Highlights an 59% In-person, and 41% Remote job distribution, with an average salary of $43,757 per year, or $21 per hour.

Software Engineer

DataVisor

Mountain View, CA

Full-time

Medical, Retirement, PTO

Re-posted 22 days ago

Job description

DataVisor is the world's leading AI-powered Fraud and Risk Platform that delivers the best overall detection coverage in the industry. With an open SaaS platform that supports easy consolidation and enrichment of any data, DataVisor's solution scales infinitely and enables organizations to act on fast-evolving fraud and money laundering activities in real time. Its patented unsupervised machine learning technology, advanced device intelligence, powerful decision engine and investigation tools work together to provide guaranteed performance lift from day one. DataVisor's platform is architected to support multiple use cases across different business units flexibly, dramatically lowering the total cost of ownership, compared to legacy point solutions. DataVisor is recognized as an industry leader and has been adopted by many Fortune 500 companies across the globe.

Our award-winning software platform is powered by a team of world-class experts in big data, machine learning, security, and scalable infrastructure. Our culture is open, positive, collaborative, and results driven. Come join us!

Summary:

As platform engineers, we are building a next-generation machine learning platform, which incorporates our secret sauce, UML (unsupervised machine learning) with other SML (supervised machine learning) algorithms. Our team works to improve our core detection algorithms and automate the full training process.

As complex fraud attacks become more prevalent, it is more important than ever to detect fraudsters in real-time. The platform team is responsible for developing the architecture that makes real-time UML possible. We are looking for creative and eager engineers to help us expand our novel streaming and database systems, which enable our detection capabilities.

We continue to push the boundary of what's possible in fraud detection and data processing at scale. Join us to help usher in more innovative solutions to the fraud detection space.

What you'll do:

  • Design and build machine learning systems that process data sets from the world's largest consumer services
  • Use unsupervised machine learning, supervised machine learning, and deep learning to detect fraudulent behavior and catch fraudsters
  • Build and optimize systems, tools, and validation strategies to support new features
  • Help design/build distributed real-time systems and features
  • Use big data technologies (e.g. Spark, Hadoop, HBase, Cassandra) to build large scale machine learning pipelines
  • Develop new systems on top of real-time streaming technologies (e.g. Kafka, Flink)

Requirements

  • 1-5 years software development experience
  • 1-5 years experience in Java, Shell, Python development
  • Excellent knowledge of Relational Databases, SQL and ORM technologies (JPA2, Hibernate) is a plus
  • Experience in Cassandra, HBase, Flink, Spark or Kafka is a plus.
  • Experience in the Spring Framework is a plus
  • Experience with test-driven development is a plus

Preferred Qualifications

  • Worked on multithreaded applications i
  • Experience in Shell and Python
  • Experience in Kubernates
  • Experience in CUDA development is a plus

Benefits

Health Insurance, 401K, PTO.

Apply