Senior Data Scientist, ML – Fraud Detection, Effectiveness

Adobe

Senior Data Scientist building fraud and abuse detection models for Adobe’s creative technology platforms. Measuring model effectiveness through experimentation, analytics, monitoring, and business-impact metrics.

Posted 9/9/2026full-timeSan Jose • California, District of Columbia, Illinois, New York, Texas, Washington • 🇺🇸 United StatesSenior💰 $133,100 - $236,400 per yearWebsite

Core Competencies

Role fit

Core Competencies

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

Demonstrates expertise in building and tuning machine learning models for fraud detection, with a strong foundation in statistical and classical ML techniques. Proficient in data analysis, model evaluation, and visualization to drive business impact and improve detection strategies.

Highest-signal resume keywords

Machine Learning Model DevelopmentStatistical AnalysisPython ProgrammingSQL ProficiencyData Visualization

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Machine LearningStatistical TechniquesModel EvaluationAnomaly DetectionClusteringBehavioral ModelingPerformance MeasurementLabeling FrameworksWeak SupervisionActive Learning

Soft Skills

Analytical JudgmentOwnershipIndependent OperationStorytelling Skills

Tools & Technologies

DashboardsData Quality ToolsModel Monitoring Systems

Industry Keywords

Fraud DetectionRisk ManagementAdversarial DomainsIdentity and TrustData Leakage

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

Build and tune ML models for fraud and abuse detection using statistical and classical ML techniques
Develop evaluation frameworks, datasets, and metrics to measure model and mitigation effectiveness
Analyze false positives/negatives, model drift, and emerging fraud patterns
Define ground truth, labeling approaches, and fraud taxonomies
Design experiments and evaluate tradeoffs across precision, recall, customer impact, and fraud loss
Build dashboards and metrics connecting detection performance to measurable business impact
Pressure-test models and data for leakage, bias, data-quality issues, and misleading results
Partner with engineering, product, policy, and risk teams to improve detection and support business decisions

Requirements

What you’ll need

8+ years in applied Data Science / ML
Experience building and evaluating production ML models
Strong foundation in statistical and classical ML, experimentation, model evaluation, and performance measurement
Strong hands-on Python and SQL skills working with large, complex datasets
Experience with model monitoring, drift, false-positive/false-negative analysis, and imperfect or delayed labels
Strong data visualization and storytelling skills
Strong analytical judgment, ownership, and ability to operate independently through ambiguity
Bachelor's or equivalent experience in Statistics, Mathematics, Computer Science, or related field
Experience in fraud, abuse, risk, identity, trust & safety, or other adversarial domains preferred
Experience with anomaly detection, clustering, behavioral modeling, or prevalence estimation preferred
Experience with labeling frameworks, weak supervision, active learning, or human-review systems preferred
Familiarity with LLMs and AI-assisted evaluation/analysis preferred
Experience evaluating multi-layered risk controls and automated decisioning systems preferred

Benefits

Comp & perks

Annual Incentive Plan (AIP) for short-term incentives
Certain roles may be eligible for a new hire equity award
Hybrid work model
Comprehensive benefits programs
Reasonable accommodations during the recruiting process