Latest AI JobsBrowse Jobs
P

Senior Data Scientist

possible-financeSeattle, WAData & Operations
Apply on company site
Salary
$175,720 to $191K.
Job type
Full-time
Work mode
Remote
Experience
Senior
Posted
17 Sept 2026
Status
Open · checked 7d ago

About this role

Based in Seattle, Washington, this hybrid Senior Data Scientist position at possible-finance suits an experienced analytical professional focused on payments performance, optimization, and fraud detection. The role is ideal for someone looking to apply data science expertise within a mission-driven fintech environment dedicated to improving financial health.

Role Overview

Possible-finance seeks a Senior Data Scientist to work in a hybrid capacity from the downtown Seattle office, requiring three days in the office each week on Monday, Tuesday, and Thursday. This professional will own the analytical systems governing money movement between the company and its customers. The position involves defining payments-health scorecards, optimizing payment timing and retry strategies around customer pay cycles, and developing risk-scoring models for payments.

Key Responsibilities

Define and build the payments-health scorecard along with channel and experiment-level anomaly monitoring.
Improve payment timing by identifying customer pay cycles and designing effective retry strategies.
Develop recurring reporting on payments fraud patterns and build risk-scoring models for payment methods.
Partner collaboratively with Engineering, Product, and Risk teams to shape the payment strategy roadmap.

Requirements

Deep background in data science fundamentals and payment domain knowledge including payment rails like ACH, RTP, card, and interchange.
Practical production machine learning experience covering model building, deployment, drift monitoring, and retraining using tools such as XGBoost and MLflow.
Strong proficiency in Python and SQL.
Experience handling large datasets in a distributed computing environment using PySpark on Databricks.
Demonstrated skills in experimentation and causal inference, alongside feature engineering instincts for transactional data.

Nice to Have

Direct fraud modeling experience.
Background in collections, recovery, or lending operations within a regulated space.
Prior cross-functional collaboration experience that directly influenced a team roadmap.
Hands-on familiarity with observability tools like Datadog.

Skills and Tools

Python
SQL
PySpark
Databricks
Datadog
XGBoost
MLflow
MLOps tooling

Salary and Benefits

Annual compensation range from $175,720 to $191,000.
Significant stock options.
Full benefits package.
Bonus plan.
Commuter benefits.
Office perks including free food and drink options.

About the Company

Possible-finance operates as a mission-driven fintech Public Benefit Corporation helping everyday Americans build financial health through fair credit access. Backed by venture investors including Union Square Ventures, Canvas Ventures, Euclidean Capital, and Unlock Venture Partners, the organization focuses on protecting consumers from predatory lending and promoting long-term economic mobility.

How to Apply

Interested candidates should apply directly on the official company careers page.

Skills and tech stack

PythonSQLPySparkDatabricksDatadogXGBoostMLflowMLOps

Questions candidates ask

Where is the Senior Data Scientist position located?

The position is a hybrid role based in downtown Seattle, Washington, requiring three days in the office each week on Monday, Tuesday, and Thursday.

What is the salary range for this role?

The compensation range is between $175,720 and $191,000, accompanied by stock options, a bonus plan, full benefits, commuter benefits, and office perks.

What technical stack is used in this role?

The tech stack includes Python, SQL, PySpark on Databricks, Datadog for monitoring, XGBoost, MLflow, and standard MLOps tooling.

What are the core technical requirements?

Requirements include production machine learning experience, understanding of payment rails, proficiency in Python and SQL, distributed computing experience with PySpark on Databricks, and experimentation and causal inference skills.

Are there any preferred or nice-to-have qualifications?

Preferred qualifications include cross-functional roadmap influence and Datadog experience. Nice-to-have qualifications include direct fraud modeling experience and a background in regulated collections, recovery, or lending operations.

Ready to apply?

The application is on the official possible-finance careers page.

Apply on company site