Data Scientist – Fraud

Jobtailor; California (MO); On-site; USD 120,000 - 170,000; Full time

Job summary

Jobtailor is seeking a product analytics professional to analyze customer and network data, build dashboards, run backtests, and generate insights driving customer growth. You will design scalable data models and collaborate with Product and Engineering to test new features.

The role emphasizes strong SQL/Python skills, data-driven decision-making, and ownership of core metrics, with exposure to fraud/risk domains as a nice-to-have asset.

Qualifications

• 3–5 years of relevant experience.
• At least 2–3 years working with product analytics, experimentation, or data-driven products.
• Strong proficiency in SQL and Python.
• Experience analyzing complex datasets and translating insights into action.
• Hands‑on experience with product analytics, experimentation, and/or backtesting methodologies.
• Experience building and maintaining dashboards, reporting frameworks, and core product metrics.
• Strong communication and stakeholder management skills.
• Experience in fraud/risk domains (nice-to-have).
• Experience developing ML models (nice-to-have).

Responsibilities

• Analyze customer and network traffic to understand Plaid Protect performance across use cases and customer segments.
• Build dashboards and metrics providing a shared view of product performance.
• Run backtests to evaluate performance and identify high-impact rules and model strategies.
• Generate insights supporting customer growth and expansion.
• Design scalable data models and schemas for reliable analysis and reporting.
• Partner with Product and Engineering to design and analyze experiments for new customer-facing features.
• Work at the intersection of product analytics, machine learning, fraud, and risk to improve product performance.
• Own metrics, dashboards, and experiments informing product strategy and decision-making.

Skills

SQL

Python

Data analysis

Dashboard development

Experimentation

Stakeholder management

Tools

Backtesting frameworks

SQL visualization tools

Job description

• Analyze customer and network traffic to understand Plaid Protect performance across use cases and customer segments
• Build dashboards and metrics providing a shared view of product performance
• Run backtests to evaluate performance and identify high-impact rules and model strategies
• Generate insights supporting customer growth and expansion
• Design scalable data models and schemas for reliable analysis and reporting
• Partner with Product and Engineering to design and analyze experiments for new customer-facing features
• Work at the intersection of product analytics, machine learning, fraud, and risk to improve product performance
• Own metrics, dashboards, and experiments informing product strategy and decision-making

Requirements

• 3–5 years of relevant experience
• At least 2–3 years working extensively with product analytics, experimentation, or data-driven products
• Strong proficiency in SQL and Python
• Experience analyzing complex datasets and translating insights into action
• Hands‑on experience with product analytics, experimentation, and/or backtesting methodologies
• Experience building and maintaining dashboards, reporting frameworks, and core product metrics
• Strong communication and stakeholder management skills
• Experience in fraud/risk domains (nice-to-have)
• Experience developing ML models (nice-to-have)

Core Competencies

Demonstrates strong expertise in product analytics, experimentation, and data‑driven decision‑making, with proficiency in SQL and Python for analyzing complex datasets. Capable of designing scalable data models and dashboards to inform product strategy and enhance performance.

Highest‑signal resume keywords

• Product Analytics
• SQL Proficiency
• Python Proficiency
• Dashboard Development
• Experimentation Methodologies

Hard Skills

• Data Analysis
• Backtesting Methodologies
• Data Modeling
• Metrics Development
• Reporting Frameworks

Soft Skills

• Strong Communication
• Stakeholder Management

Industry Keywords

• Fraud Domain
• Risk Domain
• Machine Learning Models