Data Science Manager – Fraud

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

Job summary

Jobtailor is seeking a Head of Data Science for Fraud product analytics in the United States. You will lead a team, set a 6–12 month roadmap with Product, Engineering and GTM, and translate insights into scalable fraud capabilities.

You will mentor data scientists, drive rigorous metrics, and collaborate across functions to advance risk and detection initiatives. You will work hands-on with Python, SQL, statistics, and causal inference techniques, ensuring results meet business goals and are

Qualifications

• Proven experience managing, mentoring, and developing high-performing data scientists.
• Strong technical depth in Python, SQL, statistics, product analytics, and applied modeling.
• Excellent communication and cross-functional collaboration across Product, Engineering, ML, GTM, and stakeholders.

Responsibilities

• Lead a team responsible for customer-facing data science and Fraud product analytics.
• Set the team’s 6–12-month roadmap with Product, Engineering, and GTM.
• Assign priorities and responsibilities across the team.
• Define product metrics, data, reporting, and alerting practices.
• Guide roadmap and investment decisions using measurement results.
• Establish repeatable processes for retrospectives and proofs of concept.
• Conduct data checks, define evaluation methods, and provide clear recommendations.
• Identify recurring fraud signals and product opportunities from customer analyses.
• Partner with Product and MLEs to develop scalable fraud capabilities.
• Review analytical designs, data models, code, and model evaluations.
• Coach data scientists through expectations, feedback, performance discussions, and growth opportunities.
• Use AI-assisted analysis and development tools, ensuring results are reviewed.
• Translate customer insights into scalable product capabilities and GTM opportunities.
• Raise the bar for product metrics, analytical rigor, and data foundations across Fraud.

Skills

Team Leadership

Product Analytics

Python Programming

SQL Proficiency

Fraud Domain Expertise

Cross-Functional Collaboration

Communication Skills

Mentoring and Coaching

Tools

Dbt

Graph-Based Data Systems

Model Interpretability Techniques

Causal Inference Techniques

AI-Assisted Analysis Tools

Job description

• Lead a team responsible for customer-facing data science and Fraud product analytics
• Set the team’s 6–12-month roadmap with Product, Engineering, and GTM
• Assign priorities and responsibilities across the team
• Define product metrics, underlying data, reporting, and alerting practices
• Use measurement results to guide roadmap and investment decisions
• Establish repeatable processes for customer retrospectives and proofs of concept
• Conduct data checks, define evaluation methods, and provide clear recommendations
• Identify recurring fraud signals and product opportunities from customer analyses
• Partner with Product and MLEs to develop scalable fraud capabilities
• Review analytical designs, data models, code, and model evaluations
• Contribute directly to critical investigations and analyses
• Coach data scientists through expectations, feedback, performance discussions, and growth opportunities
• Use AI-assisted analysis and development tools where useful, ensuring results are reviewed
• Translate customer insights and fraud analyses into scalable product capabilities and GTM opportunities
• Raise the bar for product metrics, analytical rigor, and data foundations across Fraud

Requirements

• Proven experience managing, mentoring, and developing high-performing data scientists
• Deep domain expertise in fraud, risk, or related areas
• Strong experience in product analytics, metric design, and measuring product performance
• Experience partnering directly with customers to deliver data-driven insights and solutions
• Strong technical depth in Python, SQL, statistics, product analytics, and applied modeling
• Demonstrated ability to set technical direction and deliver complex, high-impact initiatives through a team while remaining hands-on
• Excellent communication and cross-functional collaboration skills across Product, Engineering, Machine Learning, GTM, and customer stakeholders
• Nice-to-have: Experience working with graph-based data or systems to identify fraud patterns and improve model performance
• Nice-to-have: Experience applying causal inference techniques to complex product or risk problems
• Nice-to-have: Experience using model interpretability techniques across traditional machine learning and modern model architectures
• Nice-to-have: Experience with dbt or similar data transformation and analytics engineering tools

Core Competencies

Demonstrates strong leadership in managing and mentoring data scientists while driving product analytics and fraud detection initiatives. Possesses deep domain expertise in fraud and risk, with a focus on developing scalable product capabilities and data-driven insights.

Highest-signal resume keywords

• Team Leadership
• Product Analytics
• Python Programming
• SQL Proficiency
• Fraud Domain Expertise

Hard Skills

• Product Metrics Design
• Statistical Analysis
• Applied Modeling
• Data Evaluation Methods
• Fraud Signal Identification

Soft Skills

• Excellent Communication
• Cross-Functional Collaboration
• Mentoring and Coaching

Industry Keywords

• Fraud Analytics
• Risk Management
• Data Science
• Customer Insights
• Data-Driven Solutions

Tools & Technologies

• AI-Assisted Analysis Tools
• Graph-Based Data Systems
• Causal Inference Techniques
• Model Interpretability Techniques
• Dbt