Назад
2 дня назад

Data Scientist - Credit (Retros) (Fintech)

176 000 - 227 000$
Формат работы
hybrid
Тип работы
fulltime
Грейд
senior
Страна
US
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Описание вакансии

TL;DR
Data Scientist - Credit (Retros) (Fintech): Analyzing credit data and model performance, developing lending recommendations, and building data pipelines and production machine learning monitoring with an accent on credit risk, underwriting, and credit policy. Focus on identifying emerging risk signals, evaluating credit retrospectives, automating ETL with dbt, and guiding product and engineering decisions through quantitative analysis.

Data Scientist - Credit (Retros)

Company

Plaid Inc.

Conditions

6 days agoSalary: 176K - 227K

Skills

Candidate Availability

Required and preferred rules are kept separate and reflect the wording in the original posting.

About the Role

You will analyze credit data and model performance to identify risk signals and improve lending outcomes. You will develop customer-specific recommendations, build and maintain data pipelines, monitor production models, and communicate actionable insights that guide product and engineering decisions.

Requirements

  • 2–5 years of industry experience in a financial services data science role
  • SQL
  • Python
  • Credit risk and underwriting model development
  • Experience building and maintaining credit policies for lenders
  • Knowledge of the lending lifecycle, including marketing, acquisition, customer management, and collections or recoveries
  • Data-driven decision-making
  • Experience building data pipelines and automating ETL processes with dbt or similar tools

Responsibilities

  • Analyze client model performance, triage issues, and identify improvement opportunities
  • Identify emerging credit risk signals and evaluate credit retrospectives
  • Create tailored client recommendations using data and credit insights
  • Build and maintain data pipelines and ETL processes
  • Develop metrics, alerts, and dashboards for production machine learning models
  • Develop recommendations for incorporating cash flow insights into lending decisions
  • Improve customer adoption through custom analysis
  • Serve as a subject matter expert on credit insights and credit policy
  • Develop models and actionable insights using quantitative analysis, data mining, and visualization
  • Identify signals for machine learning models
  • Inform product and engineering decisions through analysis and presentations

Benefits

  • Equity
  • Medical insurance
  • Dental insurance
  • Vision insurance
  • 401(k)

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