slopepay.com
Data Scientist
slopepay.com, San Francisco, California, United States, 94199
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 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 creating 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 efforts 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 a related field.
Experienced with machine learning concepts 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.
#J-18808-Ljbffr
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 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 creating 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 efforts 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 a related field.
Experienced with machine learning concepts 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.
#J-18808-Ljbffr