Data Scientist, ML

Cosmos•New York, NY

1d•Hybrid

About The Position

Cosmos is building the most inspiring place on the internet: a visual discovery platform where people save the images, products, and ideas they care about, search by color or AI caption, and share collections with attribution built in. We're a Series A startup ($22M raised) building a new home for artists and creators, growing from 2M users toward 10M and beyond. Every save, search, and scroll is an event, so the data per person runs deep, and the recommendation and search systems that power discovery run on what this team builds. We're looking for a Data Scientist focused on ML to own the analytics and experimentation layer of our recommendation and discovery systems in Warsaw or New York City. You'll partner directly with our Head of Product and Staff ML Engineer to shape how millions of people find inspiration on Cosmos. This is a high-ownership role at an early-stage company. You'll design the frameworks we use to understand content relevance and user engagement, run experiments that directly influence ranking and personalization, and help us build a recommendation ecosystem that balances discovery, creator distribution, and long-term user value. If you've worked on recommendation systems at scale and want to apply that expertise somewhere you can see the full picture and move fast, this is the role.

Requirements

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 Haves

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

Responsibilities

Design and analyze experiments across our recommendation surfaces, including multi-arm A/B tests and ecosystem-level tradeoff analysis
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 algorithm development, feature analysis, and model evaluation
Quantify opportunities across our discovery ecosystem (boards, collections, trending content, creator distribution) and translate insights into roadmap priorities
Define and monitor north star metrics, building a deep understanding of user behavior and long-term engagement patterns
Influence product strategy with data—you'll have a seat at the table, not just a ticket queue