Data Scientist, Product
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What you'll do
- Get deep into Ascend's products, data infrastructure, and the metric definitions the three teams currently disagree on
- Build relationships with Product, Engineering, and the Head of Data to understand how measurement decisions get made today
- Audit the scores and thresholds already in production and map where instrumentation is missing
- Propose and settle the accuracy, success, and exception metrics for Ascend's products
- Start owning the thresholds that decide when the system acts on its own versus routing to a person
- Partner with Product and Engineering on instrumentation for at least one upcoming feature before it ships
- Fully own the accuracy, success, and exception metrics across all products
- Build and ship models that change a workflow rather than describe one: confidence scoring on extracted data, ranking and matching for reconciliation, sequencing for collections and recovery
- Monitor every score in production — predicted against realized, drift, and an explicit re-fit or retire date — and design the experiments our volume can actually power, saying plainly when a surface can't support one
- Turn product usage data into roadmap input
What they're looking for
- Owned product data inside a live workflow rather than reporting on one, and can name the decision your number changed
- Deployed a model into a live workflow and owned it afterward, including calibration, drift, and the call to re-fit or retire
- Comfortable explaining calibration, drift, predicted-versus-realized, and re-fit timing to a non-technical owner
- Strong in SQL and feature engineering
- Someone who thrives in the ambiguity of an early-stage startup and will say when the data is too weak to decide, then propose a next step anyway
- Experience working in our toolset: Slack, Notion, Excel, Front, and Linear
- Strong written and verbal communication skills, ability to quickly understand complex (and sometimes dense) subject matter, and great attention to detail
Nice to have
- Experience with payments is a plus
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
What We Do
Insurance is a $10T industry still running on PDFs, email threads, and manual reconciliation. Ascend is changing that. We built the first and only complete financial operations platform purpose-built for insurance — combining accounting automation, payments, and premium financing in a single vertically integrated solution. In five years, we've become the market leader: over 4,000 businesses, including more than half of the 50 largest brokers in the country, trust Ascend to bring clarity to complex accounting operations — from collection to close.
How We Do It
Ascend automates the financial operations that insurers, MGAs, and agencies run every day — and embeds directly into the transactions where money actually moves. Point solutions are easy to build and even easier to copy. The companies that win won't just sell tools — they'll capture value where the money actually moves. Ascend is built for exactly that moment. Our customers don't just adopt software — they unlock measurable efficiency with a proven 5–7x return on investment.
Our team is based in San Francisco and Columbus. We bring deep expertise in insurance, fintech, and financial infrastructure to build what the industry has never had.
Why We Do It
The insurance industry intermediates trillions of dollars — and almost none of that infrastructure was built for the modern era. Legacy systems aren't just inefficient; they're a ceiling on what the industry can become.
Ascend's mission is to become the leading financial automation platform for insurance by 2030 — powering the financial operations of the entire industry and transforming the way trillions of dollars move. We're not building another point solution. We're building the financial backbone of insurance.
Your Role
We're hiring a Senior Data Scientist to own measurement across the surfaces that move our customers' money. Reporting to the Head of Data, you will settle the metric definitions that three teams currently disagree on, set the thresholds that decide when a human touches a document, and stay accountable for those thresholds after they go live. We cut a standalone data scientist role to fund this one. Building a model got cheap; being answerable when a threshold is wrong did not.
Responsibilities will include
Objective #1: In your first 30 days, you will:
- Get deep into Ascend's products, data infrastructure, and the metric definitions the three teams currently disagree on
- Build relationships with Product, Engineering, and the Head of Data to understand how measurement decisions get made today
- Audit the scores and thresholds already in production and map where instrumentation is missing
Objective #2: In your first 60 days, you will:
- Propose and settle the accuracy, success, and exception metrics for Ascend's products
- Start owning the thresholds that decide when the system acts on its own versus routing to a person
- Partner with Product and Engineering on instrumentation for at least one upcoming feature before it ships
Objective #3: In your first 90 days, and beyond, you will:
- Fully own the accuracy, success, and exception metrics across all products
- Build and ship models that change a workflow rather than describe one: confidence scoring on extracted data, ranking and matching for reconciliation, sequencing for collections and recovery
- Monitor every score in production — predicted against realized, drift, and an explicit re-fit or retire date — and design the experiments our volume can actually power, saying plainly when a surface can't support one
- Turn product usage data into roadmap input
You might be a good fit if you are/have:
- Owned product data inside a live workflow rather than reporting on one, and can name the decision your number changed
- Deployed a model into a live workflow and owned it afterward, including calibration, drift, and the call to re-fit or retire
- Comfortable explaining calibration, drift, predicted-versus-realized, and re-fit timing to a non-technical owner
- Strong in SQL and feature engineering
- Experience with payments is a plus
- Someone who thrives in the ambiguity of an early-stage startup and will say when the data is too weak to decide, then propose a next step anyway
- Experience working in our toolset: Slack, Notion, Excel, Front, and Linear
- Strong written and verbal communication skills, ability to quickly understand complex (and sometimes dense) subject matter, and great attention to detail
Compensation: $150,000-$200,000
Equal Opportunity
We are committed to equal opportunity employment and embrace diversity within our organization. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
We encourage candidates to apply even if their experience doesn't precisely match this opportunity. We value diversity and are dedicated to equal opportunity employment.
Company
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