About this role
Ascend is building the first and only complete financial operations platform purpose-built for insurance, combining accounting automation, payments, and premium financing into a single vertically integrated solution. The engineering and data teams are replacing legacy systems that rely on PDFs, email threads, and manual reconciliation with modern infrastructure that handles trillions of dollars.
The incoming product data scientist will own measurement across the surfaces that move customer money, settle contested metric definitions, and manage the thresholds that determine when the system automates a task versus routing it to a human. This person takes full accountability for deployed models, tracking predicted versus realized outcomes, data drift, and re-fit schedules. Candidates who thrive in early-stage startup ambiguity, possess deep technical capabilities, and take responsibility for real-world workflow outcomes are well-suited for the position.
What you will do
- Audit existing production scores and thresholds while mapping out missing instrumentation across product data surfaces.
- Propose and establish definitive accuracy, success, and exception metrics for all company products.
- Manage the operational thresholds that dictate when automated systems execute actions independently versus routing tasks to a human operator.
- Partner closely with product and engineering teams to instrument upcoming features prior to release.
- Build and deploy models that directly alter workflows, including confidence scoring, reconciliation matching, and collections sequencing.
- Monitor production models continuously for drift and predicted versus realized performance while establishing explicit re-fit and retirement dates.
- Translate product usage data into actionable insights for the product roadmap.
What you need
- Demonstrated experience owning product data inside a live workflow and influencing tangible operational decisions.
- Track record of deploying models into production and maintaining ongoing ownership of calibration, drift, and retirement timelines.
- Strong technical proficiency in SQL and feature engineering.
- Ability to clearly explain complex statistical concepts like calibration and drift to non-technical stakeholders.
- Strong written and verbal communication skills paired with meticulous attention to detail.
Nice to have
- Experience with payments
Skills and tech stack
Questions candidates ask
Where is the team located?
The team is based in San Francisco and Columbus.
What is the compensation for this role?
The salary range for the position is $150,000 to $200,000.
What tools and software does the team use?
The team uses Slack, Notion, Excel, Front, and Linear.