Data Scientist

Data Privacy • San Francisco, CA, United States • $200k - $250k / year • 3w ago

Why work with us?

You’ll get the chance to be an early team member at a fast-growth YC startup. We truly believe it’s all about slope — not where you start but how fast we are growing and iterating
We are growing insanely fast and are well-funded. This is a once-in-a-lifetime opportunity to join our rocketship at an inflection point!
We’re a tight-knit, intense team, where you'll learn a ton and have ownership

We have raised capital from the some of the best VCs (Y Combinator, Union Square Ventures) and angels (Sam Altman and founders of Doordash, Dropbox, Plaid, Opendoor, Unity) in the world

Work with seasoned second-time founders
Help transition the massive B2B economy online and help small business owners thrive

What you'll do

Build and deploy new models and constantly iterate on core risk models by developing new features, evaluating different modeling techniques and create differentiated strategies for each customer segment
Manage large datasets, ensuring data quality, consistency, and reliability throughout the modeling process
Evaluate new private and/or public data sources; interact with vendors to assess external data products
Monitor model performance and proactively identify model deficiencies
Collaborate closely with credit risk, customer success and GTM to align data science effort with business goals; provide timely support for questions or issues impacting customer experience or credit results
Collaborate with data engineering teams to optimize data infrastructure and workflows
Prepare documentation and reports for model development and validation processes; ensure model compliance with regulatory requirements and internal risk management standards

Requirements

5+ years experience in advanced statistical modeling with strong technical skills, preferably in both model development and deployment
Master’s or Ph.D. in Quantitative Finance, Statistics, Computer Science, Mathematics, Economics, or related field
Experienced with machine learning concept and risk modeling techniques such as regression, decision trees and boosting; experience with building data products using LLM is strongly preferred
Strong skill set with Python (including sklearn, pandas, numpy, etc.) and SQL
Experience with consumer / small business lending and payments risk management (credit and/or fraud); knowledge of using alternative data sources for underwriting, such as e-commerce and accounting data, is strongly preferred
Excellent written and verbal communication skills; ability to clearly explain data science work and collaborate with stakeholders to execute
Attention to detail, strong work ethic, and a relentless drive; enjoy a fast-paced and results-driven culture
Excel and enjoy a collaborative and in-person workplace
Comfortable with handling ambiguity and creating processes from scratch. We are growing fast and still learning
A demonstrated ability to get stuff done. In an ideal world, you’ve worked in a fast-growing company and know what we should be doing today, in one year, and in two years
Self-directed and able to work with minimal supervision
Iterative mindset

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