Senior Data Scientist – Fraud Detection

DataVisor

. Lead the full lifecycle of fraud detection features and models, from ideation and data exploration to prototyping, productionizing, and monitoring.

Posted 9/24/2026full-timeMountain View • California • United StatesSenior💰 $140,000 - $170,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Expertise in fraud detection and machine learning, with a strong focus on developing predictive features from large-scale data and leading investigations into complex fraud cases. Proficient in utilizing advanced data processing tools and communicating findings effectively to diverse stakeholders.

Highest-signal resume keywords

Fraud DetectionMachine Learning LifecyclePython ProgrammingSQL ProficiencyData Processing with Spark

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Predictive ModelingLogistic RegressionGradient BoostingPattern SynthesisHypothesis Testing

Soft Skills

Excellent Communication SkillsInvestigator Mindset

Tools & Technologies

SparkHadoopAWSGCPAzure

Industry Keywords

CybersecurityFintechConsumer PaymentsBankingMarketplace Risk

Tech Stack

Tools & technologies

AWSAzureCloudCyber SecurityGoogle Cloud PlatformHadoopPySparkPythonSparkSQL

About the role

Key responsibilities & impact

Lead the full lifecycle of fraud detection features and models, from ideation and data exploration to prototyping, productionizing, and monitoring.
Develop predictive features from large-scale, multi-dimensional data, including user behavior, device intelligence, network graphs, and transaction records.
Process massive, noisy, and imbalanced datasets using Spark, SQL, and the proprietary AI platform.
Leverage agentic AI to automate analytic pipelines and develop reusable tools for fraud investigation, feature generation, and reporting.
Lead investigations into complex fraud cases across identities, accounts, devices, and transaction surfaces.
Reconstruct attacker sequences and hypothesize actor intent and tooling.
Produce evidence-backed technical reports and case studies for product, engineering, operations, legal, and executive stakeholders.
Generate customer-facing fraud trend reports by synthesizing case-level findings and aggregate data.

Requirements

What you’ll need

Master's or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field.
3+ years of applied experience in fraud detection, cybersecurity, or a related adversarial/high-velocity risk domain (fintech, consumer payments, banking, SaaS, marketplace risk, or security research).
Solid understanding of both classic machine learning models (Logistic Regression, Gradient Boosting, etc.).
Hands-on experience with the machine learning lifecycle in a production environment.
Investigator mindset: demonstrated skill in pattern synthesis, hypothesis testing, and triaging signal from noise in ambiguous, adversarial cases — not just building and monitoring models.
Strong programming skills in Python (must-have) and proficiency with SQL; experience with PySpark is a significant plus.
Experience with large-scale data tools (Spark, Hadoop, etc.) and cloud platforms (AWS, GCP, Azure).
Excellent communication skills — able to explain complex, ambiguous, or technical behavior clearly to both technical and non-technical audiences, including customers and executives.
Professional proficiency in written and spoken English, with the ability to collaborate effectively in a global, cross-functional team.

Benefits

Comp & perks

PTO
Stock Options
Health Benefits