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Fraud Detection Machine Learning Jobs in Bodega Bay, CA

Acceler8 Talent

Research Engineer

Santa Rosa, CA · On-site

$300K/yr

... detect and conduct a novel class of AI-powered cyber operation. This is not conventional ... and applied machine learning. You'll work with petabyte-scale, multimodal time-series data ...

II-VI Incorporated

Manufacturing Supervisor

Santa Rosa, CA · On-site

$66K - $90K/yr

Ensure a learning environment is in place, and your team is trained and developed to consistently ... Knowledge of automation, machining and other manufacturing processes * Lean Manufacturing ...

Coherent

Manufacturing Supervisor

Santa Rosa, CA

$66K - $90K/yr

Ensure a learning environment is in place, and your team is trained and developed to consistently ... Knowledge of automation, machining and other manufacturing processes * Lean Manufacturing ...

Coherent Corp.

Manufacturing Supervisor

Santa Rosa, CA · On-site

$66K - $90K/yr

Ensure a learning environment is in place, and your team is trained and developed to consistently ... Knowledge of automation, machining and other manufacturing processes * Lean Manufacturing ...

All JobsFraud Detection Machine Learning JobsFraud Detection Machine Learning Jobs in Bodega Bay, CA

Fraud Detection Machine Learning information

See Bodega Bay, CA salary details

$12

$21

$31

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

As of Aug 21, 2026, the average hourly pay for fraud detection machine learning in Bodega Bay, CA is $21.42, according to ZipRecruiter salary data. Most workers in this role earn between $17.69 and $22.84 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 cities near Bodega Bay, CA are hiring for Fraud Detection Machine Learning jobs?

Cities near Bodega Bay, 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 Bodega Bay, CA as of August 2026, with employment types broken down into 100% Full Time. Highlights an 60% In-person, and 40% Remote job distribution, with an average salary of $44,561 per year, or $21.4 per hour.

Fraud Data Analyst

Cloud think technologies LLC

Bodega Bay, CA • On-site

Contractor

Re-posted 28 days ago

Job description

Must have: Development to design and implement fraud monitoring solutions

and fraud prevention strategies.
◦ Analyze large datasets to identify trends, patterns, and anomalies related to gift
card fraud.
◦ Develop and maintain dashboards and reports that monitors suspicious activities
trends.
◦ Create and track fraud KPIs to measure the effectiveness of fraud prevention
strategies.
◦ Proactively identify emerging gift card fraud schemes and develop strategies to
mitigate them.
◦ Collaborate with other fraud analysts, data scientists, and engineers to develop
fraud prevention tools, systems and products."

Overview

We are seeking an experienced Fraud Data Analyst specializing in gift card fraud to join our team in the Bay Area. In this critical role, you will leverage your advanced PySpark expertise (6–9 years) and strong analytical skills to develop and implement robust fraud monitoring solutions that protect our customer's business. You will work cross-functionally with Fraud, Legal, Operations, Product, and Business Development teams to proactively detect, analyze, and mitigate complex gift card fraud schemes.

Key Responsibilities

  • Collaborate with cross-functional teams including Fraud, Legal, Operations, Product, and Business Development to design and implement scalable fraud monitoring solutions and dynamic fraud prevention strategies.
  • Analyze large-scale transactional and behavioral datasets using advanced PySpark to uncover trends, patterns, and emerging anomalies related to gift card fraud.
  • Develop and maintain insightful dashboards and regular reports to monitor suspicious activity trends and fraudulent behaviors.
  • Create and track fraud-specific Key Performance Indicators (KPIs) to measure and optimize the effectiveness of fraud prevention initiatives.
  • Proactively identify and assess new and emerging gift card fraud schemes, recommending and executing risk mitigation strategies.
  • Partner with other fraud analysts, data scientists, and engineers to build and refine fraud prevention tools, machine learning models, and monitoring systems.
  • Communicate analytical findings and recommendations clearly to both technical and non-technical stakeholders.

Required Qualifications:

  • Advanced-level proficiency in PySpark (6–9 years of hands-on experience), including large-scale data processing and ETL workflows.
  • Demonstrated experience in fraud detection, ideally with a focus on gift card fraud.
  • Strong skills in data analysis, pattern recognition, and statistical modeling.
  • Professional experience collaborating with cross-functional business and technical teams.
  • Excellent written and verbal communication skills.

Preferred Qualifications:

  • Proficiency in SQL and Hadoop for data extraction, manipulation, and analytics.
  • Experience developing dashboards and reports using BI tools (e.g., Tableau, PowerBI).
  • Familiarity with building or evaluating machine learning models for fraud detection.

Description:

The services You will provide the Deloitte project team: As a Data Analytics Contractor, you will analyze and interpret complex data sets to provide actionable insights and support decision-making processes. Collect, process, and analyze large datasets to identify trends, patterns, and insights. Develop and maintain data models, dashboards, and reports to visualize data findings. Utilize statistical and analytical tools to perform data analysis and generate insights. Collaborate with project teams to understand data requirements and deliver relevant analytical solutions. Ensure data accuracy and integrity by performing data validation and quality checks.

Enable Skills-Based Hiring

No

Primary Skill Required for the Role

 

Pyspark

Level Required for Primary Skill

 

Advanced (6-9 years’ experience)

Additional Details for Role

 

Data Analyst
"◦ Collaborate with cross-functional teams such as Fraud, Legal, Operations, Product
and Business