Data Scientist – Credit

Plaid

. Deep-dive into model performance for clients, triage issues, and identify opportunities to improve outcomes .

Posted 11/2/2026full-timeNew York City • New York • United StatesJuniorMid-Level💰 $176,400 - $226,800 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in credit risk analysis and the development of underwriting models, leveraging SQL and Python for data-driven decision-making. Proficient in building data pipelines and automating ETL processes to enhance model performance and client outcomes.

Highest-signal resume keywords

Credit Risk AnalysisSQL ProficiencyPython ProgrammingData Pipeline DevelopmentETL Process Automation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Quantitative AnalysisData MiningData VisualizationMachine Learning Model DevelopmentCredit Policy Implementation

Soft Skills

Clear CommunicationInfluencing StakeholdersProblem Solving

Tools & Technologies

Dbt

Industry Keywords

Financial ServicesLending LifecycleCustomer ManagementCredit ExpertiseUnderwriting Models

Tech Stack

Tools & technologies

ETLPythonSQL

About the role

Key responsibilities & impact

Deep-dive into model performance for clients, triage issues, and identify opportunities to improve outcomes
Analyze Plaid’s financial data to identify emerging credit risk signals and build and evaluate credit retrospectives
Create tailored recommendations for clients based on their data and Plaid’s insights
Build and maintain data pipelines, including ETL processes using tools such as dbt
Develop metrics, alerts, and dashboards to monitor production machine learning model performance
Develop recommendations for incorporating cash flow insights into lending decisions
Improve customer adoption of Plaid Insights through custom analysis
Serve as a subject matter expert on credit insights and credit policy implementation
Apply quantitative analysis, data mining, and data visualization to develop models and actionable insights
Identify useful signals that can inform machine learning models
Inform and influence Product and Engineering teams through clear analysis and presentations

Requirements

What you’ll need

2–5 years of industry experience in a financial services Data Science role
Deep familiarity with SQL and Python
Deep understanding of credit risk and the development of underwriting models
Hands-on credit expertise building and maintaining credit policies for lenders
Knowledge of the lending lifecycle, including marketing, acquisition, customer management, and collections or recoveries
Demonstrated ability to make data-driven decisions
Experience building data pipelines and automating ETL processes with dbt or similar tools (nice to have)

Benefits

Comp & perks

Equity
Medical insurance
Dental insurance
Vision insurance
401(k)