1

Fraud Detection Machine Learning Jobs in California

Stay in touch

Redwood City, CA · On-site

$140K - $168K/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 ...

Senior Software Engineer

Mountain View, CA · On-site

$144K - $190K/yr

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 ...

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 ...

Senior Software Engineer

Mountain View, CA · On-site

$144K - $190K/yr

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 ...

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 ...

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 ...

Senior Software Engineer

Mountain View, CA

$144K - $190K/yr

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 ...

The role involves using machine learning models for web content categorization and fraud detection. Responsibilities : • Developing predictive models in the area of marketing • Understanding ...

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 ...

Own small to medium components of machine learning systems from technical designthrough ... Knowledge in domains such as recommender systems, fraud detection, personalization, and marketing ...

Own small to medium components of machine learning systems from technical designthrough ... Knowledge in domains such as recommender systems, fraud detection, personalization, and marketing ...

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

Showing results 21-40

Fraud Detection Machine Learning information

See California salary details

$10

$17

$26

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

As of Aug 12, 2026, the average hourly pay for fraud detection machine learning in California is $17.82, according to ZipRecruiter salary data. Most workers in this role earn between $14.71 and $18.99 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 California? For Fraud Detection Machine Learning jobs in California, the most frequently searched job titles are:
What job categories do people searching Fraud Detection Machine Learning jobs in California look for? The top searched job categories for Fraud Detection Machine Learning jobs in California are:
What cities in California are hiring for Fraud Detection Machine Learning jobs? Cities in California with the most Fraud Detection Machine Learning job openings:

Stay in touch

TigerGraph

Redwood City, CA • On-site

$140K - $168K/yr

Full-time

Re-posted 18 days ago


Job description

TigerGraph is a platform for advanced analytics and machine learning on connected data. TigerGraph's core technology is the only scalable graph database for the enterprise. 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 accelerate their analytics, AI, and machine learning:
  • Seven out of the top ten global banks use TigerGraph for real-time fraud detection.
  • Over 50 million patients receive care path recommendations to assist them on their wellness journey.
  • 300 million consumers receive personalized offers with recommendation engines powered by TigerGraph.
  • TigerGraph reduces power outages by optimizing the energy infrastructure for 1 billion people.

Join the graph and analytics revolution!