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Senior Data Scientist — Data Foundations & AI

Plaid

Recently Verified
Data ScienceFintechSenior (6-10 yrs)HybridFintech Startup

Best for experienced data scientists wanting to work on financial data classification and fraud detection at massive scale.

AI → Finance

Location

New York, NY / San Francisco, CA

Salary Range

$174,000 – $260,000

Posted

Mar 5, 2026

Insider Signals

Beta
📊Active Hiring

Plaid has had 14 hiring events in the last 30 days — they're actively building.

Company AI Context

AI Maturity

established

Tech Stack

pythonsqlml-modelingawsinternal-data-platformfeature-stores

Build ML models powering Plaid's financial data infrastructure — fraud detection, transaction classification, and data enrichment at billion-transaction scale.

## About the Role Plaid is hiring a Senior Data Scientist to work on Data Foundations & AI, developing ML models that power the financial data infrastructure used by thousands of fintech apps. You will build models for fraud detection, data enrichment, and financial data classification at massive scale. ## Responsibilities - Design and implement ML models for fraud detection, transaction categorization, and data quality - Build and improve classification systems processing billions of financial transactions - Develop experimentation frameworks to measure model performance and business impact - Collaborate with engineering teams to deploy models in production at scale - Research and apply state-of-the-art techniques in NLP and tabular data modeling ## Requirements - 5+ years of experience in data science or ML engineering - Strong proficiency in Python, SQL, and ML frameworks - Experience with NLP, classification systems, or fraud detection models - Track record of deploying ML models in production - Experience working with financial data or payments systems preferred - MS or PhD in a quantitative field preferred

Skills & Technologies

PythonNLPFraud DetectionClassification SystemsSQLFinancial DataExperimentation

Best Backgrounds

  • data science
  • ML engineering
  • fraud analytics
  • financial data engineering

Related Titles to Explore

  • Staff Data Scientist
  • ML Engineer - Fraud
  • Financial Data Scientist
  • Applied Scientist - Payments

Career Bridge Playbook

Beta

Career paths into this role

data scientist
ML engineer
applied scientist
quantitative analyst

Where this role leads

Principal Data Scientist
Head of Data Science
ML Platform Lead
Data Science Director

Transition Difficulty

medium

Key Skill Gaps to Close

  • financial data taxonomy
  • fraud detection signal patterns
  • open banking regulations

Skills used in this role

Technical

pythonsqlml-modelingfraud-detectiondata-enrichmenttransaction-categorizationa-b-testing

Domain

fintech-data-infrastructurefraud-detectionfinancial-data-classificationopen-banking

Soft Skills

cross-functional-influencementorshipdata-storytelling

Interested in this role?

Apply directly on the company's career page.

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