Data Scientist - Credit (Retros)

Description

The company builds the tools and products developers use to connect people's financial accounts to apps and services, working with thousands of companies including banks and fintechs. Its network covers 12,000 financial institutions across the US, Canada, the UK and Europe. Founded in 2013, it is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London and Amsterdam.

The role sits in the Data organization, where the team builds models and insights from the company's financial data to help lenders make better decisions and create stronger credit products.

You will work with data science, engineering, product and Go-to-Market colleagues to turn complex data into practical recommendations and scalable solutions. The team values technical rigor, creativity, user empathy and teamwork.

As a Data Scientist on the Credit team, you will use quantitative analysis and credit expertise to help lenders get more value from cash flow insights. Your work covers client problems, credit policy and data products, turning the company's data into recommendations that shape business impact and product direction.

Responsibilities

• Dig into model performance for clients, diagnose problems and find ways to improve results.
• Analyze the company's data to spot emerging credit risk signals, then build and evaluate credit retrospectives.
• Write tailored recommendations for each client based on their data and the company's insights.
• Build and maintain data pipelines, including ETL work in dbt or similar tools.
• Create metrics, alerts and dashboards that track how production machine learning models perform.
• Own the development of practical guidance on using cash flow insights in lending decisions, following account-level decisioning best practices.
• Drive customer adoption of the company Insights through analysis suited to each customer's needs.
• Act as the subject matter expert on credit insights and credit policy implementation, supporting Data Science, Engineering and Go-to-Market.
• Apply quantitative analysis, data mining and visualization to build models and actionable insights.
• Find useful signals that can feed machine learning models.
• Guide Product and Engineering decisions through clear analysis and presentations.

Requirements

• 2, 5 years of industry experience as a Data Scientist in financial services.
• Deep familiarity with SQL and Python.
• Deep understanding of credit risk and underwriting model development.
• Hands-on experience building and maintaining credit policies for lenders.
• Knowledge of the full lending lifecycle, including marketing, acquisition, customer management and collections or recoveries, and how to improve each stage.
• A record of making data-driven decisions.
• Nice to have: experience building data pipelines and automating ETL with dbt or similar tools.
• You are encouraged to apply even if your background does not match every item, since strong qualifications can come from prior work or lived experience.

Conditions

The company is an equal opportunity employer and does not discriminate on the basis of protected characteristics. It considers qualified applicants with criminal histories in line with applicable law. Accommodations are available in recruiting on request.

Benefits

• Pay depends on scope and responsibilities, the candidate's experience and skills, and location. Equity and/or commission may be offered depending on the role.
• Comprehensive benefits include medical, dental, vision and 401(k). Pay and benefits may change at any time under the terms of the applicable plans.