About Finosu

Finosu is an AI-native loan servicer. What is that? A servicer handles the communications and payment rails for lenders. The servicing industry services over 10 trillion dollars of debt a year.

Loan servicing is the perfect application for AI. Borrowers want help on their time, not during call center hours, and AI makes 24/7 support real for the first time. It also makes compliance better, not worse: every interaction follows the rules, every disclosure is delivered, and everything is logged and auditable. What makes us different is how we measure success. We are not judged on generic CSAT scores. We are judged on dollars generated for our lenders, and it turns out the two go together: borrowers who are treated well perform better.

We know this problem because we lived it. Our founders built a $200M consumer lender together, owning the end-to-end stack from origination through servicing and collections. We felt every broken part of it firsthand: the vendors that didn't care about outcomes, the compliance gaps, the borrowers who fell through the cracks. So we are building the thing we wished we had.

Things to Know

We are a start-up and our processes are in flux. Priorities might shift more abruptly than at a more establish company.

We’re in office, we have found that collaboration especially in a context-rich field is important
You will own the data architecture for a platform handling millions of loan balances and millions of LLM-driven borrower interactions.

About the Role

This is a unique role in that we are looking for a data engineer with strong analytical and product mindset. An ideal candidate would come up with hypothesis and create the tooling needed to verify or invalidate their hypothesis. We have a small team of less than 10 people.

In this role

Define the data strategy and how Finosu collect or leverage data to build a competitive advantage
You won’t always have the benefit of statistical significance, so intuition and taste will be important
Closely report to the founders about the results of the experiments
30/60/90
- 30 days: you will evaluate our communications strategies and determine how to increase our conversion rate. We do about millions of touch points per month across our borrowers.
- 60 days: you will run A/B tests with our AI agents on borrowers on the platform.
- 90 days: you will evaluate and develop with our stack for data analysis. Our current stack is Python/Postgres, and you will lead the evaluation of our first warehouse (Snowflake vs BigQuery). You will design the indexing and retrieval strategy to ensure our LLMs can query borrower history in <200ms during live voice interactions.

Projects people in this role have taken or will take:

Evaluated our communication strategies and optimize them
Evaluate when we will move to data lakes or data warehouse
Rearchitect our data schema for reporting
Self serve analytics

About You

We have a roadmap, but you’ll own your work: propose what to build, challenge priorities when you see a higher-impact path, and help the team ship what best serves our users.
You learn quickly—aptitude and attitude matter more than years of experience.
You love measuring things. As we move fast, the solutions to many challenges are hidden in the data.
You’re product-minded and data-minded. In this role, you’ll make many decisions to build the best product across the projects you own.

About the company

Finosu is an AI-native loan servicer. What does that mean? Well, a servicer handles the communications and payment rails for lenders, think of it as customer support for lenders.

Where does the AI-native piece come in?

Usually, all that communication is staffed by thousands of humans in remote place like the Philippines, India and other English-speaking countries where people are doing arbitrage on cheap English speakers (one co-founder grew up near a couple). Now let’s remove all the humans doing that. Not just from the front-office, but the back office as well. At Finosu, we believe superior customer support comes from removing humans when there is no more bottlenecks on business hours, and everything is compliant default.

Technology

NextJS frontend Python/Typescript Backend PostgreSQL Redis

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