Sr Data Scientist – Financial Crimes Detection, PayPal World

PayPal

Hybrid

Quick summary

Work type: Hybrid
Location: Scottsdale, Arizona · Omaha, Nebraska
Salary: $123,500–$183,700 / yr
Posted: 67 days ago
Nearby: 93 roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $154k

This role $154k

$89k most similar roles pay here $195k

This role pays less than 52% of similar roles. Most pay $126,800–$181,855 — the shaded band above. At the midpoint, this role pays about $154k versus about $154k for comparable roles.

Based on 240 similar postings.

Employer

About PayPal

PayPal is a leading global digital wallet and online payment system, founded in 1998, that allows individuals and businesses to send, receive, and manage funds securely in over 200 markets.

PayPal currently has 84 open roles on FindRole.

Listed pay typically runs $160,500–$235,826 across 84 roles with salary data.

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At a glance

TL;DR · Sr Data Scientist – Financial Crimes Detection, PayPal World

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As a Senior Data Scientist on PayPal World’s financial crimes detection program, you will design and implement advanced monitoring solutions to combat money laundering and fraud across the platform's multi-wallet payment rails. You will work closely with data engineering teams to define data requirements for new transaction types, develop feature engineering pipelines in BigQuery, and build machine learning models using Python and frameworks like TensorFlow or XGBoost. This role requires a deep understanding of AML regulatory frameworks and experience working with complex payment network data. Operating within PayPal’s Global Financial Crimes and Customer Protection team, you will collaborate with compliance policy teams across multiple jurisdictions to ensure detections meet local regulatory obligations while providing actionable intelligence for investigators. Ideal candidates have 5+ years of relevant experience in building AML/fraud detection systems at financial institutions or fintechs, along with strong SQL skills and proficiency in Python for data analysis and model development.

Skills

Python BigQuery Snowflake pandas scikit-learn TensorFlow XGBoost SQL Airflow CI/CD AML FATF BSA/FinCEN AUSTRAC EU AMLD Graph-based analysis Network detection methods Feature engineering pipelines Model validation frameworks BigQuery ML

What you'll do

Design and implement transaction monitoring rules to detect money laundering and fraud.
Develop feature engineering pipelines in BigQuery for detection use cases.
Analyze cross-jurisdictional transaction patterns to identify new risk typologies.
Build model validation frameworks including back testing against historical data.
Translate detection outputs into actionable intelligence for compliance investigators.
Collaborate with compliance policy teams across multiple jurisdictions on regulatory obligations.
Advise product and engineering teams on financial crime risks before feature launches.

What we're looking for

Minimum 5 years of experience in AML/fraud detection models or transaction monitoring systems.
Strong SQL skills for complex analytical queries on large-scale datasets.
Proficiency in Python for data analysis, feature engineering, and model development.
Experience with BigQuery ML, TensorFlow, XGBoost, or similar frameworks.
Understanding of AML regulatory frameworks and how detection systems comply.
Comfort operating in ambiguity and defining workstreams independently.
Strong communication skills to explain model behavior to non-technical stakeholders.