About the role...

We're looking for our first Data Scientist to own the analytics and experimentation layer of our recommendation and discovery systems. You'll partner directly with our Head of Product and Staff ML Engineer to shape how millions of people find inspiration on Cosmos.

The core focus of the role is product analytics and experimentation. You will design and analyze A/B tests, define success metrics, and help us understand how changes to ranking, personalization, and discovery affect user behavior and the overall ecosystem. This role is not focused on dashboards for reporting only, and it is not a back seat analytics support function. We are especially excited about candidates who can go a step beyond analysis.

If you enjoy applied ML and want to contribute to feature engineering or lightweight modeling, there will be room to do so in close partnership with ML Engineering. This role is ideal for someone who enjoys working across analytics, experimentation, and applied ML in a small team, and who is excited about building foundational data practices from the ground up.

What you'll do:

• Own experimentation and analysis for recommendation and discovery surfaces

• Design and analyze experiments across our recommendation surfaces, including multi-arm A/B tests and ecosystem-level tradeoff analysis

• Define, monitor, and evolve core and north star metrics to a build deep understanding of user behavior, engagement loops, and long-term value.Define and monitor core product and recommendation metrics, Influence product strategy with data—you'll have a seat at the table, not just a ticket queue

• Quantify opportunities across our product surfaces and translate analytical insights into clear product and ranking decisions, using data to directly shape product strategy and prioritization.

• Develop frameworks to explain and improve content ranking and relevance—understanding why certain content gets promoted (or doesn't) for specific users

• Partner with ML Engineering on feature engineering, model development and evaluation

• Spend ~70 to 80 percent of your time on product analytics and experimentation and ~20 to 30 percent partnering with ML Engineering on applied recommendation work

What we're looking for:

• 7+ years of experience in data science, with significant time spent on recommendation systems, ranking, or personalization

• Strong experimentation chops—you've designed and analyzed complex A/B tests, understand causal inference, and can navigate ecosystem-level tradeoffs

• Proficiency in SQL and Python, with experience working on web-scale data

• Track record of influencing product strategy and shipping improvements based on your analysis

• Experience partnering closely with ML engineers on algorithm development and evaluation

• Ability to communicate clearly to technical and non-technical audiences—you tell stories with data, not just present charts

• Comfort with ambiguity and excitement about building foundational data practices at an early-stage company

Nice to have:

• Experience at a visual discovery, social, or content platform (Pinterest, Instagram, TikTok, etc.)

• Background in balancing organic and paid/promoted content distribution

• Experience with contextual bandits, reinforcement learning, or online learning systems

Benefits & perks:

• Premium health, dental, and vision

• 20 days PTO, plus company holidays, plus a 2-week winter break

• Top-spec MacBook Pro, Apple Studio Display

• Monthly stipend for software and tools

• Monthly team events

• Fully covered commute in NYC

• Daily lunch & dinner stipend at the office

• Optional Superpower membership