Senior Data Scientist

Possible Finance; Seattle (WA); Hybrid; USD 176,000 - 191,000; Full time

Benefits offered by this job

Stock options

Full benefits

Bonus plan

Commuter benefits

Free drinks and food at office

Job summary

Possible Finance in Seattle is seeking a Senior Data Scientist to own the data science behind how money moves, building the payments-health scorecard and monitoring to surface anomalies across channels and experiments. You will define how we time payments and design retry strategies that align with our customers’ pay cycles, reducing failed payments and improving recovery.

You will work in Python, SQL, and PySpark on Databricks, with Datadog for monitoring, and collaborate with Engineering,

Qualifications

• Depth in data science fundamentals and payments domain knowledge.
• Hands-on production ML development with model deployment and monitoring.
• Strong Python and SQL and experience with large datasets in PySpark.

Responsibilities

• Owns data science behind how money moves at Possible.
• Defines the payments-health scorecard and monitoring to surface anomalies.
• Designs retry strategies to align payments with customers' pay cycles and reduce failed payments.
• Redefines how payments fraud is understood and develops recurring reporting and risk models.
• Partners with Engineering, Product, and Risk to embed payment strategy in the roadmap.

Skills

Python

SQL

PySpark

Databricks

XGBoost

MLflow

Datadog

Experimentation

Causal inference

Payments rails

Tools

Databricks

Datadog

MLflow

XGBoost

Job description

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.

The Role & Impact

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.

What You’ll Bring

Requirements

Must-Have

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

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.

Nice-to-H have

Direct fraud modeling experience and a background in collections, recovery, or lending operations in a regulated space are nice to have.

How we work. We expect you to act with ownership—you'll be defining what healthy payments means here, not waiting for a spec. We take a scientific approach: rapid experimentation, intellectual honesty, and a willingness to change your mind when the data says so. And this role is mission-driven in a concrete way, because the strategies you design touch real people's bank accounts. We optimize for customers ending up better off, not just for dollars collected.
Possible Finance is on a mission to help communities break the debt cycle and unlock economic mobility for generations to come. With the backing of our venture investors (Union Square Ventures, Canvas Ventures, Euclidean Capital, Unlock Venture Partners), a loyal following of hundreds of thousands of customers, and a fantastic team, we're unwavering in our fight for financial fairness. As one of only a few fintech Public Benefit Corporations, we've baked our dual commitment to building a profitable and socially impactful company right into our charter; we only succeed when our customers do too. If you'd like to help us ship financial products that protect consumers from predatory lending practices and promote financial health, give us a shout.

This is a Hybrid position. We work in the office three days a week (Monday, Tuesday, Thursday). Our office is in downtown Seattle.

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.