Description
We are seeking an experienced Senior Machine 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!
Responsibilities
- 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.
Requirements
- 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: 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.