Senior Data Scientist, ML- Fraud Detection & EffectivenessThe OpportunityWe are seeking an experienced
SeniorMachine Learning Data Scientist to build
fraud and abuse detection models and measure how effectively they work. This role combines hands-on modeling with deep experimentation, evaluation, and analytics to improve detection and quantify business impact.
You will work across the fraud lifecycle - from modeling and ground-truth definition to performance measurement, monitoring, and executive-ready insights!
What you'll Do- Build and tune ML models for fraud and abuse detection using statistical and classical ML techniques.
- Develop robust evaluation frameworks, datasets, and metrics to measure model and mitigation effectiveness.
- Analyze false positives/negatives, model drift, and emerging fraud patterns to continuously improve detection.
- Define ground truth, labeling approaches, and fraud taxonomies that support reliable model development and evaluation.
- Design experiments and evaluate tradeoffs across precision, recall, customer impact, and fraud loss.
- Build dashboards and metrics that translate detection performance into measurable business impact.
- Pressure-test models and data for leakage, bias, data-quality issues, and other sources of misleading results.
- Partner across engineering, product, policy, and risk teams to turn insights into detection improvements and business decisions.
What you'll need to succeed- 8+ years in applied Data Science / ML, with 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 - able to translate complex analysis into clear insights and recommendations.
- Strong analytical judgment, ownership, and ability to operate independently through ambiguity.
- Bachelor's or equivalent experience in Statistics, Mathematics, Computer Science, or related field; advanced degree a plus.
Preferred Attributes- Experience in fraud, abuse, risk, identity, trust & safety, or other adversarial domains.
- Experience with anomaly detection, clustering, behavioral modeling, or prevalence estimation.
- Experience with labeling frameworks, weak supervision, active learning, or human-review systems.
- Familiarity with LLMs and AI-assisted evaluation/analysis.
- Experience evaluating multi-layered risk controls and automated decisioning systems.
Hybrid Work Model: This role follows a hybrid schedule, with a minimum of 3 days per week in the office.
Expected Pay Range:Our compensation reflects the cost of labor across several U.S. geographic markets, and we pay differently based on those defined markets. The U.S. pay range for this position is $133,100 -- $236,400 annually. Pay within this range varies by work location and may also depend on job-related knowledge, skills, and experience. Your recruiter can share more about the specific salary range for the job location during the hiring process.
In California, the pay range for this position is $163,200 - $236,400In New York, the pay range for this position is $163,200 - $236,400In Illinois, the pay range for this position is $149,100 - $216,000In Washington, the pay range for this position is $157,900 - $228,575
At Adobe, for sales roles starting salaries are expressed as total target compensation (TTC = base + commission), and short-term incentives are in the form of sales commission plans. Non-sales roles starting salaries are expressed as base salary and short-term incentives are in the form of the Annual Incentive Plan (AIP).
In addition, certain roles may be eligible for long-term incentives in the form of a new hire equity award.
