Product Analyst

Description:

As a Product Analyst / BI (AI & Data) supporting one of the company’s newly acquired portfolio companies, you will build its data and analytics foundation from the ground up, working directly with the CEO and engineering team. The company operates at real scale, with roughly 5 million users a month, so this role works with meaningful data volume from day one.

This is not a traditional reporting role. You will help instrument events, design data schemas, and work with engineering to build the underlying data pipeline, while also running ongoing, dynamic analysis of financial and user data rather than static, periodically reviewed dashboards. You will also help set up and evaluate AB tests for statistical significance alongside the engineering team.

The core of this role is product analytics: funnels, activation, retention, and segmentation. This is the hardest part to find and the area we weight most heavily, followed by solid financial modeling ability. The ideal candidate is also an AI-native builder who thinks and works with AI as a core part of how they operate, not someone who simply knows a BI tool, and is comfortable in close, real-time collaboration in a fast-moving, chat-driven environment, working directly alongside the CEO and engineering rather than independently. We are prioritizing talent and curiosity over years of experience, ideally someone with a technical and finance hybrid background (for example, a computer science major with a finance minor, or a finance major with a computer science minor); a strong fit on the core priorities matters more than checking every box, the rest can be learned on the job.

Job Responsibilities:

Own product analytics end to end, funnels, activation, retention, and segmentation, translating user behavior into insights the CEO and team can act on quickly.
Build solid financial models and reporting on subscription, billing, and revenue data to support business decisions.
Collaborate in real time with the CEO and engineering team through fast, chat-based back-and-forth, proposing ideas and engaging in direct discussion to reach the best outcome quickly.
Build and evolve an AI-native analytics stack, using AI tools natively as part of the analysis process rather than as an add-on.
Help instrument product and business events, and design data schemas, in partnership with engineering to build the company’s core data foundation.
Work with engineering on the data pipeline to ensure clean, reliable data flows from source systems (subscription, Stripe billing, Banana ERP) into usable structures.
Set up and evaluate AB tests for statistical significance, working closely with engineering to ensure tests are configured and read correctly.
Run ongoing, dynamic analysis of financial and user data, iterating quickly rather than producing static, periodically reviewed dashboards.
Support ad hoc analysis and shifting priorities, staying comfortable wearing multiple hats in a small, fast-moving team.
Ensure accuracy and consistency of data used for reporting, analysis, and testing.
 

Qualifications for Success:

Strong, demonstrated ability in product analytics, funnels, activation, retention, and segmentation; this is the core of the role and the top priority in our search.
Solid financial modeling and reporting ability.
Comfortable working in a real-time, chat-driven environment with fast iteration, working directly with the CEO and engineering team rather than independently.
Demonstrated, hands-on experience using AI tools natively in analytical or technical work, not simply familiarity with AI as a concept.
A strong fit on the priorities above matters more than checking every box below; we are prioritizing talent and curiosity over years of experience, and the rest can be learned on the job.
Bachelor’s degree in Computer Science, Finance, Data Analytics, or a related field, ideally with meaningful exposure to both (for example, a CS major with a finance minor, or a finance major with a CS minor).
Comfort working with event data, schema design, and data pipelines, or strong aptitude and interest in learning this alongside engineering.
Strong foundation in statistics, with the ability to set up and evaluate AB tests for statistical significance.
Working knowledge of SQL and comfort working with large, high-volume datasets.
Familiarity with monitoring and observability tools such as DataDog is a plus, given close day-to-day collaboration with engineering.
Comfort learning and adapting to whichever BI or visualization platform the team selects; the specific tool is still being evaluated.
Familiarity with SaaS, subscription-based businesses, or recurring revenue models, along with exposure to Stripe billing or ERP/accounting systems such as Banana ERP, will be considered an advantage.
Strong communication skills, with the confidence to propose ideas, give and receive direct feedback, and iterate quickly with the CEO and engineering team.
High attention to detail, with a builder’s mindset suited to a small, fast-moving team wearing multiple hats, and a willingness to learn and grow rapidly as the company’s AI-first BI and product analytics function is built out.