Data Scientist - Fraud
Plaid
Responsibilities
- Work at the intersection of product analytics, machine learning, and fraud/risk to drive meaningful product improvements.
- Own the metrics, dashboards, and experimentation frameworks that inform product strategy and decision-making.
- Analyze complex datasets to uncover clear, actionable insights that shape product direction.
- Partner with go-to-market teams to demonstrate the technical and business value of our products to customers.
Qualifications
- 3–5 years of total experience, including at least 2–3 years working deeply with product analytics, experimentation, or data-driven products.
- Strong proficiency in SQL and Python.
- Hands-on experience with product analytics, experimentation frameworks, or backtesting methodologies.
- Skilled in designing, building, and maintaining dashboards and core product performance metrics.
- Capable of designing and interpreting backtests or offline evaluations for ML and rules-based systems.
- Excellent communicator with strong stakeholder-management skills across diverse teams.
- Background in fraud or risk domains — Nice to have.
- Familiarity with data-insights products and a solid understanding of model-performance metrics — Nice to have.
- Exposure to customer-facing or GTM-facing analytics — Nice to have.
176400 - 243600 USD a year
The target base salary for this position ranges from $176,400/year to $243,600/year [in Zone 1, in Zone 4 or encompassing all Zones]. The target base salary will vary based on the job's location.
Our geographic zones are as follows:
Zone 1 - San Francisco / New York City / Seattle
Zone 2 - Los Angeles / Washington DC / Austin / Boston / Sacramento / San Diego
Zone 3 - Atlanta / Portland / Chicago / Philadelphia / Denver / Miami / Dallas / Raleigh
Zone 4 - All other US cities
The base salary range listed for this full-time position excludes commission (if applicable), equity and benefits. The pay range shown on each job posting is the minimum and maximum target for new-hire salaries. Actual pay may be higher or lower depending on factors like skills, experience, and relevant education or training.