Data Scientist

Possible Finance•Seattle, WA

17h•$161,000 - $175,000•Hybrid

About The Position

Since our founding, we have redefined how people approach small-dollar loans—delivering over $1 billion in funding to more than 1.5 million customers, issuing over 4 million loans, and saving our customers more than $650 million. At Possible, we’re building a new type of consumer finance company; one that helps our customers stay out of debt rather than profit from their staying in it. We are a Public Benefit Corporation with the mission to help communities unlock economic mobility through affordable credit products crafted to improve financial health for generations. Join the team that’s making our goal a reality. Team Introduction Our data science team sits at the center of a growing company, building the models, tools, and analytical infrastructure that power how Possible makes decisions, monitors risk, and operates with rigor at scale. We are seeking a Data Scientist to work at the intersection of analytics, model governance, and internal tooling – growing our capacity to support the teams and systems that help Possible move fast and make good decisions. The Role You will own a structured framework to evaluate new data sources and modeling techniques. Your analyses will include clear assumptions that guide our data vendor budget and investment decisions. You will build out model monitoring and governance infrastructure — turning it from a reactive compliance exercise into a proactive utility. Working cross-functionally, you'll collaborate with internal teams to develop analytical tools and automations that ease bottlenecks. This enables other teams to access data independently and find answers without relying on advanced analytics repeatedly.

Requirements

2–4 years of hands-on experience in data science or analytics
Strong Python and SQL skills for analysis and automation
Proven statistical foundation: you can frame a hypothesis, develop a meticulous test, and communicate results — including their limitations — to non-technical partners
Strong data visualization skills
A service mentality: you build things people actually use, you get happiness from unblocking others, and you know how to balance rigor with shipping

Nice To Haves

Background in consumer finance or credit risk
Experience with model governance or model validation
Experience with AI or LLM platforms in an applied context
Track record supporting cross-functional teams with analytical work
Testing or QA mentality — validating that what was built does what it should
Experience with dbt or Airflow
Dashboard design and management experience

Responsibilities

You will own a structured framework to evaluate new data sources and modeling techniques.
Your analyses will include clear assumptions that guide our data vendor budget and investment decisions.
You will build out model monitoring and governance infrastructure — turning it from a reactive compliance exercise into a proactive utility.
Working cross-functionally, you'll collaborate with internal teams to develop analytical tools and automations that ease bottlenecks.
This enables other teams to access data independently and find answers without relying on advanced analytics repeatedly.