ZipRecruiter

Log In

1

Graph Anomaly Detection Jobs in California (NOW HIRING)

Plaid Inc

Senior Fraud Research Analyst

San Francisco, CA · On-site

$139K - $191K/yr

Experiment with network/graph analysis, sequence mining, anomaly detection, and custom heuristics where off-the-shelf approaches fail Ecosystem Monitoring & Knowledge Leadership * Continuously survey ...

Cisco

Applied AI Scientist

San Diego, CA

Advanced Anomaly Detection with Graph: Track record developing hybrid graph-temporal approaches (e.g., GNN + Transformer, graph contrastive learning, dynamic graph forecasting) for detecting ...

Cisco

Applied AI Scientist

Roseville, CA

Advanced Anomaly Detection with Graph: Track record developing hybrid graph-temporal approaches (e.g., GNN + Transformer, graph contrastive learning, dynamic graph forecasting) for detecting ...

Cisco

Applied AI Scientist

Irvine, CA

Advanced Anomaly Detection with Graph: Track record developing hybrid graph-temporal approaches (e.g., GNN + Transformer, graph contrastive learning, dynamic graph forecasting) for detecting ...

Cisco

Applied AI Scientist

Carlsbad, CA

Advanced Anomaly Detection with Graph: Track record developing hybrid graph-temporal approaches (e.g., GNN + Transformer, graph contrastive learning, dynamic graph forecasting) for detecting ...

Cisco

Applied AI Scientist

Santa Monica, CA

Advanced Anomaly Detection with Graph: Track record developing hybrid graph-temporal approaches (e.g., GNN + Transformer, graph contrastive learning, dynamic graph forecasting) for detecting ...

Walmart

(USA)Staff, Data Scientist

Sunnyvale, CA · On-site

Apply graph-based and spatiotemporal modeling where relationships matter: GNNs, temporal graphs ... outliers, anomaly detection, and regime changes. * Hands-on experience with deep learning ...

Walmart

(USA)Staff, Data Scientist

Milpitas, CA · On-site

$143K - $286K/yr

Apply graph-based and spatiotemporal modeling where relationships matter: GNNs, temporal graphs ... outliers, anomaly detection, and regime changes. * Hands-on experience with deep learning ...

Walmart

(USA)Staff, Data Scientist

Mountain View, CA · On-site

$143K - $286K/yr

Apply graph-based and spatiotemporal modeling where relationships matter: GNNs, temporal graphs ... outliers, anomaly detection, and regime changes. * Hands-on experience with deep learning ...

Walmart

(USA)Staff, Data Scientist

Hayward, CA · On-site

$143K - $286K/yr

Apply graph-based and spatiotemporal modeling where relationships matter: GNNs, temporal graphs ... outliers, anomaly detection, and regime changes. * Hands-on experience with deep learning ...

Walmart

(USA)Staff, Data Scientist

Fremont, CA · On-site

$143K - $286K/yr

Apply graph-based and spatiotemporal modeling where relationships matter: GNNs, temporal graphs ... outliers, anomaly detection, and regime changes. * Hands-on experience with deep learning ...

Walmart

(USA)Staff, Data Scientist

Sunnyvale, CA · On-site

$143K - $286K/yr

Apply graph-based and spatiotemporal modeling where relationships matter: GNNs, temporal graphs ... outliers, anomaly detection, and regime changes. * Hands-on experience with deep learning ...

Walmart

(USA)Staff, Data Scientist

San Jose, CA · On-site

$143K - $286K/yr

Apply graph-based and spatiotemporal modeling where relationships matter: GNNs, temporal graphs ... outliers, anomaly detection, and regime changes. * Hands-on experience with deep learning ...

Walmart

(USA)Staff, Data Scientist

San Mateo, CA · On-site

$143K - $286K/yr

Apply graph-based and spatiotemporal modeling where relationships matter: GNNs, temporal graphs ... outliers, anomaly detection, and regime changes. * Hands-on experience with deep learning ...

Walmart

(USA)Staff, Data Scientist

Cupertino, CA · On-site

$143K - $286K/yr

Apply graph-based and spatiotemporal modeling where relationships matter: GNNs, temporal graphs ... outliers, anomaly detection, and regime changes. * Hands-on experience with deep learning ...

Primis

Software Engineer (San Francisco)

San Francisco, CA · On-site

$150K - $250K/yr

Transaction monitoring and anomaly detection * A new transactional foundation that connects and automates a company's financial graph This team ships fast, talks to customers early, and builds from ...

Discord

Senior Data Engineer, Ads

San Francisco, CA · On-site

$124K - $169K/yr

Develop data quality frameworks, monitoring systems, automated anomaly detection, and SLA ... Experience with Conversion Data and APIs, MMP integrations, or identity graph infrastructure is ...

Discord

Senior Data Engineer, Ads

San Francisco, CA · On-site

$220K - $245K/yr

Develop data quality frameworks, monitoring systems, automated anomaly detection, and SLA ... Experience with Conversion Data and APIs, MMP integrations, or identity graph infrastructure is ...

previous pagenext page

Showing results 21-40

All JobsGraph Anomaly Detection JobsGraph Anomaly Detection Jobs in California

Graph Anomaly Detection information

What is graph anomaly detection?

Graph anomaly detection is the process of identifying unusual patterns, nodes, edges, or substructures within graph data that deviate from expected behavior. These anomalies could indicate fraud, security breaches, network failures, or other significant events in applications such as cybersecurity, social networks, and financial transactions. Techniques in graph anomaly detection leverage statistical, machine learning, and deep learning approaches to analyze complex relationships and structures within graph data. Detecting these anomalies helps organizations quickly address potential threats or irregularities. The field is interdisciplinary, combining knowledge from data science, graph theory, and domain-specific expertise.

What are the key skills and qualifications needed to thrive in graph anomaly detection?

To excel in Graph Anomaly Detection, you need strong skills in data science, graph theory, and machine learning, typically supported by a degree in computer science or a related field. Proficiency with Python, libraries like NetworkX or PyTorch Geometric, and familiarity with graph databases such as Neo4j are commonly required. Analytical thinking, problem-solving abilities, and attention to detail help professionals identify subtle anomalies and communicate findings effectively. These skills ensure accurate detection of unusual patterns, supporting cybersecurity, fraud prevention, and other critical applications.

What are common challenges faced by professionals working in graph anomaly detection, and how can they be addressed?

Professionals in Graph Anomaly Detection often face challenges such as handling large-scale, high-dimensional graph data and distinguishing between legitimate anomalies and noise. Collaborating closely with data engineers and domain experts is essential to ensure accurate labeling and effective feature engineering. Additionally, staying updated with rapidly evolving algorithms and tools is important for optimizing detection performance. Regular cross-functional meetings and ongoing training can help address these challenges and support continuous improvement.

What is the difference between Graph Anomaly Detection vs Data Scientist?

AspectGraph Anomaly DetectionData Scientist
Required CredentialsDegree in Computer Science, Data Science, or related fields; knowledge of graph theory and machine learningDegree in Statistics, Computer Science, or related fields; proficiency in programming and data analysis
Work EnvironmentResearch labs, tech companies, industries analyzing network dataBusiness, finance, tech firms analyzing large datasets for insights
Industry UsageSpecialized in detecting irregularities in graph-structured dataBroadly used for data analysis, predictive modeling, and decision-making

While both roles involve data analysis and machine learning, Graph Anomaly Detection focuses specifically on identifying irregularities within graph-structured data, whereas Data Scientists work across various data types and analytical tasks. Understanding these differences helps organizations choose the right expertise for their data challenges.

What are popular job titles related to Graph Anomaly Detection jobs in California?

For Graph Anomaly Detection jobs in California, the most frequently searched job titles are:

What job categories do people searching Graph Anomaly Detection jobs in California look for?

The top searched job categories for Graph Anomaly Detection jobs in California are:

What cities in California are hiring for Graph Anomaly Detection jobs?

Cities in California with the most Graph Anomaly Detection job openings:

Graph Anomaly Detection jobs near you
Infographic showing various Graph Anomaly Detection job openings in California as of August 2026, with employment types broken down into 85% Full Time, 11% Part Time, and 4% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution.

Senior Fraud Research Analyst

Plaid Inc

San Francisco, CA • On-site

$139K - $191K/yr

Full-time

Medical, Dental, Vision, Retirement

Re-posted 28 days ago

Job description

We believe that the way people interact with their finances will drastically improve in the next few years. We're dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid's network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam.
Our Fraud team's mission is to help companies detect and prevent fraud using Plaid's financial network data. We believe that transaction patterns, device signals, identity linkages, and behavioral data are dramatically underleveraged tools in fraud prevention. Our products - including Protect and Signal - operate at network scale and depend on real-world investigation and research to stay ahead of adaptive adversaries.
As a Senior Fraud Researcher, you will sit at the intersection of live fraud investigation, applied data science, and product innovation. You will lead complex investigations, translate findings into detection improvements, and collaborate tightly with Data Science, ML, and Product teams to shape the next generation of Plaid's fraud capabilities. This is not a purely operational role - your research directly drives features, model inputs, and product design.
Responsibilities:
Live Fraud Investigation & Reconstruction
  • Lead investigations into complex fraud cases across identities, accounts, devices, and transaction surfaces
  • Provide support to day-to-day fraud operations including SEVs and alert triage
  • Reconstruct attacker sequences and hypothesize actor intent and tooling
  • Distill patterns from noisy signals into clear narratives and actionable insights
  • Bridge investigation outcomes to product and model improvements

Signal & Tool Utilization at Scale
  • Operate across Plaid's fraud tooling - dashboards, alerting systems, network signals, and analytics platforms - to detect and validate anomalies
  • Stress-test existing capabilities, identify systemic gaps, and define new detection primitives
  • Proactively identify gaps in internal fraud tooling and automation, driving enhancements to improve efficiency and scale

Product & Model Partnership
  • Collaborate with Data Science, ML/AI, and Product teams to improve labeling, feature sets, evaluation frameworks, and model decay monitoring
  • Surface data quality limitations and systematically formalize missing features
  • Translate exploratory research into reusable feature pipelines, model inputs, or rule augmentations
  • Participate in product discovery, roadmap planning, and post-launch evaluation to ensure fraud-awareness by design

Deep Applied Fraud Research
  • Conduct longitudinal and structural analysis of how fraud types manifest in Plaid network data - entity linkages, temporal patterns, attack rotations, tool chains
  • Experiment with network/graph analysis, sequence mining, anomaly detection, and custom heuristics where off-the-shelf approaches fail

Ecosystem Monitoring & Knowledge Leadership
  • Continuously survey external fraud trends, adversary techniques, tooling, and emerging threat vectors
  • Proactively perform threat modeling of abuse surfaces and initiate research proposals when patterns emerge

Case Studies & Reporting
  • Produce clear, evidence-backed technical reports and case studies for product, engineering, operations, legal, and executive stakeholders
  • Document investigation workflows, attack classifications, and proof-of-concept detection logic
  • Drive post-incident learning by capturing lessons from fraud incidents and feeding them back into defenses

Qualifications:
  • 3+ years of applied fraud experience in a high-velocity environment (fintech, consumer payments, banking, SaaS, marketplace risk, or security research)
  • Investigator mindset: pattern synthesis, hypothesis testing, and skilled triage between signal and noise
  • End-to-end investigation experience reconstructing attacker intent and behavior in multi-step attack sequences across accounts, devices, and identities
  • Post-containment incident response experience with a deep emphasis on post-mortems and root cause analysis
  • Dark and grey-web navigation and investigation experience; ability to assess source credibility and translate external intelligence into actionable insights
  • Strong communication: ability to explain complex, ambiguous behavior to technical and non-technical audiences
  • Tool fluency with data environments and investigative toolchains (BI tools, anomaly detection, case trackers)

Preferred
  • SQL for deep data querying and exploratory analysis
  • Python for scripting, rapid prototyping, and analytical workflows
  • Graph/network analysis experience to detect linked behavioral structures or actor networks
  • Familiarity with rule engines, signal gating, and large-scale monitoring systems
  • Experience applying AI tools and agents to accelerate investigations and research workflows
  • Ability to translate fraud research into actionable signals, rules, or labeled datasets that improve model performance

Nice to Have
  • Fraud domain certifications (e.g., CFE)
  • Prior work on consumer identity, payments, or risk platform development
  • Exposure to production ML model lifecycles and metrics for drift/decay
  • Experience improving internal fraud tooling, automation, or case management systems

Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven individuals who care deeply about making the financial ecosystem more equitable. We recognize that strong qualifications can come from both prior work experiences and lived experiences. We encourage you to apply to a role even if your experience doesn't fully match the job description. We are always looking for team members that will bring something unique to Plaid!
Plaid is proud to be an equal opportunity employer and values diversity at our company. We do not discriminate based on race, color, national origin, ethnicity, religion or religious belief, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, military or veteran status, disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state, and local laws. Plaid is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance with your application or interviews due to a disability, please let us know at accommodations@plaid.com.
Please review our Candidate Privacy Notice here.
Additional compensation in the form(s) of equity and/or commission are dependent on the position offered. Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k). Pay is based on factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience and skillset, and location. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans.

Apply