Senior Product Analyst, Payments

About Owner.com

Owner is the AI growth system for local restaurants.

Owner's AI continuously improves SEO, marketing, and online ordering to grow first-party orders. Unlike other companies that force small business owners to master their software to drive sales, Owner gives them a proven system run by experts.

Owner is like having an army of engineers and marketers on your side, just like the big chains.

Our vision

We're starting by helping independent restaurants succeed online.

Most local businesses are struggling with these same problems. Huge technology corporations are taking their customers, bleeding their profits, and making it hard for them to survive.

Once we nail the solution for restaurants we'll scale it into every other local business type.

In the future we envision tens of millions of local business owners using our technology to succeed in the digital age.

Our traction

Since 2020, we've generated tens of millions in revenue and processed over half a billion dollars of online orders. 1 in 5 Americans have used Owner.com.

More importantly, we've helped over 20,000 restaurant owners, and saved them nearly $200 million in fees.

Our team

Our team is now in the low hundreds. We've got top talent from the most successful companies in SMB software, including Shopify, HubSpot, DoorDash, ServiceTitan, Rappi, Faire and Stripe.

We'll be scaling even faster in 2026 to keep pace with our customer growth.

Where we work

Owner is a remote-first, global company headquartered in San Francisco, with a sales hub in Toronto. For a few of our roles we prioritize in-person collaboration at one of our office locations. Most of our teammates are distributed throughout the globe. Please review the role description and discuss with your recruiter for more details on location.

Why we're looking for you

We're building an effective, impactful Product Analytics function at Owner.com. As a Risk & Payments Data Scientist, you will play a pivotal role in shaping the product roadmap through close collaboration with Product Managers to establish proper metrics and impact sizing. You will drive experimentation initiatives and manage the testing framework. You will be instrumental in designing and implementing KPIs for our payments product squad, identifying gaps and potential opportunities that will help grow the business. The best candidates will not just provide insights to the EPD (Engineering & Product) org, but be a strategic driver, identifying gaps / opportunities in the money & risk area, building internal alignment around them, and creating meaning business impact.

We're migrating from Stripe Standard to Custom Connect / Adyen for Platforms, which means we'll own far more of the risk surface : merchant onboarding / underwriting, fraud detection, dispute mitigation, reserves, and payout risk. As a Risk Data Scientist you will be building the models, signals, and decisioning that protect our merchants and our P&L—while keeping conversion high and friction low.

For this particular role we are focused on candidates located in the San Francisco Bay Area. We are a remote-first company with a home base in SF, where our team comes together for periodic in-person collaboration (most local teammates opt to come in on Tuesdays / Thursdays). For more details chat with your recruiter!

The impact you will have

Payments modeling

Cut losses without killing conversion : Ship ML models that reduce fraud / chargebacks and credit losses while maintaining checkout auth rates and onboarding pass-through.

Accelerate safe growth : Create merchant risk scores and dynamic controls (e.g., reserves / holdbacks, payout delays) that scale to 10k+ restaurants.

Give Ops superpowers : Build signals, alerts, and tools that let our Payments Ops / Risk Ops team review what matters—and automate the rest.

Make risk measurable : Define loss budgets and risk SLIs / SLOs; deliver dashboards that make risk tradeoffs explicit.

Build and maintain ML models for merchant underwriting, transaction fraud, chargeback propensity, payout risk .

Design reusable frameworks for feature generation, model training, deployment, and monitoring so we can add new models quickly without reinventing the wheel.

Payments analytics

Own analytics for payments, billing, and risk features , from user checkout experience to internal financial reporting.

Monitor and improve critical KPIs such as payment success rate, failed payment recovery, fraud rates, chargeback volume, and revenue leakage. Set up monitoring for drift, stability, and business KPIs, with automated alerts.

Identify and size revenue & risk opportunities across the payments funnel (from checkout to Stripe to invoice collection).

Partner with Product Managers on AB tests and experiments related to payments UX, fraud flags, or risk workflows.

Collaborate with Engineering to instrument new product features and ensure great event tracking and data integrity in payment flows.

Enhance product planning influencing product planning through informative impact sizing, enabling more strategic decision-making.

Improve data Integrity and quality : Collaborate with developers on database design to strengthen data integrity and quality.

Establish a Single Source of Truth (SSOT) : Work alongside Data & Analytics Engineers to implement robust models in DBT and Snowflake, and design dashboards that provide a unified view of business-critical data. Integrate third-party and processor signals (Stripe Radar, Adyen RevenueProtect, device / identity data) into our models.

Who you'll work with

Reporting Structure : This role reports directly to our Director of Data Analytics, Piotr Rosiak.

Technical Collaboration : You will collaborate with Analytics Engineers on all technical aspects, including data modeling, data quality, and the use of tools like DBT and Snowflake.

Work hand-in-glove with Payments Ops & Risk Lead to encode policy into models, define review queues, and reduce manual workload.

Collaborate with Payments PM / GM on onboarding UX, step-up flows, and dispute tooling; quantify conversion vs. loss tradeoffs with clear, dollarized impact.

Provide merchant-level insights (watchlists, risk cohorts) and playbooks (what to hold, what to terminate, what to educate).

What we're looking for

4–8+ years in applied ML or risk data science (fintech / payments, marketplace, or anti-fraud).

Hands-on with Python , SQL , and ML libraries / frameworks ; comfortable with MLflow (or equivalent), and feature stores.

Proven track record shipping production models that materially reduced losses or improved conversion; strong offline evaluation + online experimentation skills.

Deep familiarity with payments / risk concepts : KYC / KYB, underwriting, auth vs capture, chargebacks, friendly fraud, card testing, reserves, payout returns , soft / hard declines.

Strength in feature engineering on messy, imbalanced data; rigorous cost-sensitive evaluation (ROC / PR, cost curves, business impact).

Excellent communicator who can turn model output into clear decisions and dollarized tradeoffsb>

Strong grasp of metrics design, experimentation, and product funnel analysis .

Ability to handle ambiguity, deep dive into financial, and proactively flag problems before they escalate.

KPI Development : A proven track record of developing KPIs and metrics tailored for product squads, particularly within a startup environment.

Data Product Expertise : Ability to build comprehensive end-to-end data products.

Organizational Skills : Highly organized with a keen eye for precision.

Industry Experience : Prior experience in SaaS and / or startup environments is highly preferred. Experience in the restaurant industry is an added advantage.

Pay and benefits

The estimated base salary range for this role is $200k – $240k, plus a generous equity pre-IPO equity package.

Other benefits include comprehensive health coverage, unlimited PTO – plus extra fun perks!

Notice – Employment Scams

Communication from our team regarding job opportunities will only be made by an Owner team member with an @owner.com email address.

We do not conduct interviews over email or chat platforms, and we will never ask you to provide personal or financial information such as your mailing address, social security number, credit card numbers or banking information. If you believe you are being contacted by a scammer, please mark the communication as "phishing" or "spam" and do not respond.

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