🎨 About OpenArt

OpenArt is an AI Storytelling and Visual Creation Platform used by millions worldwide. We’re building the next generation of creative tools powered by cutting-edge AI, enabling anyone to create videos, visuals, characters, and stories with unprecedented speed and imagination. We believe the future of creativity is AI-native, and we’re shaping that future.

🚀 Why Join OpenArt

• First Data Scientist hire in the US — set the bar, shape the function, and build the foundation of OpenArt's data science practice.
• Direct impact on product strategy — your work shapes what we build, how we prioritize, and how we measure success.
• Work across product, engineering, data, marketing, and finance — one of the most cross-functional roles in the company.
• Build from 0 → 1 — define how we use data to drive product decisions at OpenArt.
• High ownership, low process, fast iteration environment.
• 7–10X revenue growth over the past 2 years — now scaling our data and analytics infrastructure to match.

🎯 About the Role

We're looking for a Data Scientist to be our first DS hire in the US (we have a Data Analyst based in Shenzhen) and help build OpenArt's data science function from the ground up.

This role sits at the intersection of product, analytics, and experimentation — focused on turning data into product decisions that move the needle for millions of creators.

You'll work closely with cross-functional teams including Product, Engineering, Data Engineering, Marketing, and Finance. By applying your technical skills, analytical mindset, and product intuition, you will help our customers improve their creative experience and help OpenArt identify and solve product development's biggest challenges.

🛠 What You’ll Do

• Product leadership: use data to shape product development, quantify new opportunities, set goals, identify upcoming challenges, and ensure the products we build bring value to our customers.
• Analytics: develop hypotheses and employ a diverse toolkit of rigorous analytical approaches — different methodologies, frameworks, and technical techniques — to test them.
• Experimentation: design, run, and analyze A/B tests and other causal experiments; help establish the experimentation culture and standards across the company.