Senior Data Scientist at Possible in Seattle, WA

Job description

• Application
• Team Introduction
• Possible is a mission-driven fintech company helping everyday Americans build financial health through access to fair, affordable credit. Our data team sits at the center of a growing company, building the models, metrics, and experimentation infrastructure that powers how Possible makes decisions, measures performance, and operates with rigor at scale.
• We are seeking a Senior Data Scientist to work at the intersection of payments performance, optimization, and fraud, owning the analytical systems that govern how reliably money moves between Possible and our customers. This role expands our capacity to treat repayment as a designed experience rather than a back-end process, building payments that work with the rhythm of our customers' financial lives.
• You will own the data science behind how money moves at Possible. You'll define and build the payments-health scorecard the company runs on, along with the monitoring that surfaces anomalies at the channel and experiment level within days. You'll own how we time payments, sharpening how we identify a customer's pay cycle and designing retry strategies that work with it rather than against it, so that more payments clear on the first attempt: fewer failed-payment fees for customers, better recovery for the business. And you'll redefine how Possible understands payments fraud, building recurring reporting on the patterns that matter and developing a model that scores the risk of a new payment method or a payment that may not clear.
• You'll work in Python, SQL, and PySpark on Databricks, with Datadog for monitoring and standard MLOps tooling for deployment. You'll partner with Engineering, Product, and Risk to develop the payment strategy as an input to the engineering roadmap.

Responsibilities

• Own the data science behind how money moves at Possible.
• Define and build the payments-health scorecard the company runs on, along with the monitoring that surfaces anomalies at the channel and experiment level within days.
• Own how we time payments, sharpening how we identify a customer's pay cycle and designing retry strategies that work with it rather than against it, so that more payments clear on the first attempt: fewer failed-payment fees for customers, better recovery for the business.
• Redefine how Possible understands payments fraud, building recurring reporting on the patterns that matter and developing a model that scores the risk of a new payment method or a payment that may not clear.
• Work in Python, SQL, and PySpark on Databricks, with Datadog for monitoring and standard MLOps tooling for deployment.
• Partner with Engineering, Product, and Risk to develop the payment strategy as an input to the engineering roadmap.

Requirements

• Requirements
• This role requires depth in the data science fundamentals and payments domain knowledge. You should have experience with modeling, production monitoring, and experiment design, and an in-depth understanding of payment rails (ACH, RTP, card, and interchange) and payment behavior. Hands-on production ML development is essential: you have built a model, deployed it, watched it drift, and retrained it, using tooling like XGBoost and MLflow or their equivalents. You bring strong Python and SQL, plus comfort with large datasets in a distributed environment such as PySpark on Databricks, experimentation and causal inference skills, and the judgment to know which method a question calls for, as well as feature engineering instincts for transactional data. You hold a high bar for your own work: you understand your data before you draw conclusions from it, and you'd rather find the flaw in your analysis yourself.
• Preferred experience includes a track record of cross-functional collaboration that has shaped another team's roadmap rather than just informed it, and hands-on experience with observability tooling such as Datadog.
• Direct fraud modeling experience and a background in collections, recovery, or lending operations in a regulated space are nice to have.

Skills

Required

• Modeling
• Production monitoring
• Experiment design
• Payment behavior
• Hands-on production ML development
• XGBoost
• MLflow
• Python
• SQL
• PySpark on Databricks
• Experimentation and causal inference

Preferred

• Cross-functional collaboration
• Observability tooling such as Datadog
• Direct fraud modeling

Benefits

• The compensation range for this role is $175,720 to $191,000. We also offer significant stock options, full benefits, a bonus plan, commuter benefits, and a very desirable office with free drink and food options.