Senior Data Scientist, ML – Fraud Detection, Effectiveness

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

• 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

• 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

Core Competencies

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 Development
• Statistical Analysis
• Python Programming
• SQL Proficiency
• Data Visualisation

ATS Optimization Keywords

Hard Skills

• Machine Learning
• Statistical Techniques
• Model Evaluation
• Anomaly Detection
• Clustering
• Behavioral Modeling
• Performance Measurement
• Labeling Frameworks
• Weak Supervision
• Active Learning

Soft Skills

• Analytical Judgment
• Ownership
• Independent Operation
• Storytelling Skills

Industry Keywords

• Fraud Detection
• Risk Management
• Adversarial Domains
• Identity and Trust
• Data Leakage

Tools & Technologies

• Dashboards
• Data Quality Tools
• Model Monitoring Systems