Senior Data Scientist, Feed Relevance

Patreon•New York, NY

2d•Hybrid

About The Position

Patreon is a media and community platform where over 300,000 creators give their biggest fans access to exclusive work and experiences. We offer creators a variety of ways to engage with their communities and build a lasting business including: paid memberships, free memberships, community chats, live experiences, and selling to fans directly with one-time purchases. Ultimately our goal is simple: fund the creative class. And we're leaders in that space, with: $8 billion+ in revenue generated since Patreon's inception 60 million+ free new memberships for fans who may not be ready to pay just yet, and 10 million+ fans paying each month for exclusive access to creators' work and community. We're continuing to invest heavily in building the best creator platform with the best team in the creator economy and are looking for a Data Scientist to support our mission. This role is based in New York or San Francisco and open to those who are able to be in-office 2 days per week on a hybrid work model. The Relevance team owns feed ranking and recommendations across Patreon's core surfaces — Home Feed, Membership Feed, Post Page Recommendations, and Niches. We're building the systems that decide what content fans see and in what order, with the goal of making every fan's experience on Patreon feel personally relevant. Our cross-functional pod includes ML Engineers, Product Managers, Backend Engineers, Client Engineers, and Data Scientists working together to improve content discovery, deepen fan engagement, and drive creator monetization. We're at an exciting inflection point: our ranking and recommendation systems are being built largely from scratch, which means you'll have outsized influence on the technical direction and measurement frameworks that shape how millions of fans discover creators.

Requirements

Bachelor's degree in Statistics, Economics, Engineering, Mathematics, a related quantitative field, or equivalent practical experience.
You have experience working on a content feed, ranking system, or recommendation platform — you understand retrieval, scoring, and the tradeoffs involved in optimizing for multiple objectives.
You are an experimentation expert, especially with A/B testing on ranking or recommendation changes, and possess a very strong statistical inference skillset.
You have experience building metrics and reporting infrastructure that teams rely on as a source of truth (dashboards, data quality pipelines, alerting).
You have solid analytical thinking — you dig into cross-platform inconsistencies, engagement anomalies, and data quality issues until you find the root cause.
You have experience making tradeoffs between speed and accuracy in fast-moving, ambiguous environments.
You are a SQL expert and have experience in Python or R to perform statistical analyses.
You have a track record of building strong relationships with product, ML engineering, and data engineering teammates.
You have a growth mindset and aspire to continuously improve your soft and technical skills.
You are an expert communicator with a love for clarity and simplicity.

Responsibilities

You'll own the analytics foundation for Patreon's feed — building and maintaining the trustworthy metrics infrastructure that powers dashboards, experiment analysis, ML training data, and leadership reporting.
You'll design and analyze ranking experiments across Home Feed, Membership Feed, Post Page Recs, and Niches, working closely with ML Engineers to evaluate relevance changes and new ranking signals.
You'll conduct exploratory analyses to surface data-backed hypotheses about feed health, content mix, engagement patterns, and discovery conversion — and translate those into actionable ranking improvements.
You'll develop feed health and engagement dashboards with drill-downs by surface, user segment, platform (iOS vs. Android), and feed version.
You'll be a thought partner to Product and ML Engineering, helping the team prioritize ranking experiments and measure what matters — from scroll depth and CTR to Discovery Attributed TPV.
You'll be a cultural and technical leader on the data science team.