Senior ML Fraud Detection Scientist

Jobtailor; California (MO); On-site; USD 150,000 - 210,000; Full time

Job summary

Jobtailor is seeking a senior data scientist to build and tune ML models for fraud detection, collaborating with risk, policy, and engineering teams to deploy scalable solutions.

You will design experiments, define ground truth, monitor drift, evaluate precision/recall tradeoffs, and build dashboards that tie detection performance to measurable business impact. 8+ years of experience, strong Python/SQL skills, and experience with labeling systems and anomaly detection are required.

Qualifications

• 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 Python and SQL skills with large, complex datasets.
• Experience with model monitoring, drift analysis, and false/true label handling.
• Strong data visualization and storytelling skills.
• Independent worker with solid analytical judgment.

Responsibilities

• Build and tune ML models for fraud and abuse detection.
• Develop evaluation frameworks, datasets, and metrics.
• 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, and business impact.
• Build dashboards linking detection performance to business outcomes.
• Pressure-test models and data for leakage, bias, and data quality issues.
• Partner with engineering, product, policy, and risk teams to improve detection.

Skills

Machine Learning Model Development

Statistical Analysis

Python Programming

SQL Proficiency

Data Visualization

Education

Bachelor's degree in Statistics/Mathematics/Computer Science or related field

Tools

Dashboards

Data Quality Tools

Model Monitoring Systems

Job description

Jobtailor is seeking a senior data scientist to build and tune ML models for fraud detection, collaborating with risk, policy, and engineering teams to deploy scalable solutions.

You will design experiments, define ground truth, monitor drift, evaluate precision/recall tradeoffs, and build dashboards that tie detection performance to measurable business impact. 8+ years of experience, strong Python/SQL skills, and experience with labeling systems and anomaly detection are required.