Senior Data Scientist - Credit

Plaid’s Data team builds models that help millions of people better understand and grow their financial lives. We’re looking for Machine Learning Engineers who have experience applying modern machine learning techniques—such as natural language processing, anomaly detection, optimization, and time series forecasting—across a range of product areas.  We value not only strong technical expertise, but also creativity, user empathy, and a collaborative mindset.

You’ll be a data scientist as a part of the Data org, contributing to diverse, high-impact data insights and machine learning challenges. Specifically, you will focus on driving business impact, and production directions through data analysis and potentially help evaluate and improve ML solutions and systems. If you're interested in building state-of-the-art Data/ML solutions to unblock financial freedom for everyone, let's chat!

The target base salary for this position ranges from $190,800/year to $286,800/year in [Zone 1].  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

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.

Own the process for creating hands on recommendations for incorporating cash flow insights into lending decision using best practice for account level decisioning.

Improve customer usage of Plaid insights data through custom analysis  adoption specific customer need.

Act as a subject matter expert for our credit insights credit policy implementation supporting data science, engineering, Go-to-Market.

Applying your expertise in quantitative analysis, data mining, and data visualization to build models and insights.

Identifying useful signals that can be fed into machine learning models.  

Informing and influencing product and engineering teams through your data analysis and presentations.

5–10 years of experience in data science within the financial services industry.

Deep familiarity with SQL and python.

Deep understanding of credit risk and the development of underwriting models

Hands on credit expertise building and maintaining actual credit policies for lenders

Knowledge of lending lifecycle and how to optimize each step (marketing, acquisitions, customer management, collections/recoveries)

Demonstrated ability to make data driven decisions