Suno
Staff Data Scientist, Mobile Consumer Create
Los Angeles · Staff+ · Full-time
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About the role
- Finding joy through being creative is inherent in human nature.
- We are hiring a data scientist to be a thought leader on this new team.
Responsibilities
- Partner with product and engineering to shape the roadmap for Mobile Consumer Create, from early ideation through launch
- Design and run experiments end-to-end that test new ways to make creation on mobile more fun and rewarding
- Build analyses that surface user behavior patterns and inform how we think about and build for our consumers. Identify the "magic moments" that keep people creating, and the product changes that lead to more of them
- Experience running experiments end-to-end, from design through analysis to shipping decision
Requirements
- 6-8+ years of experience in data science in consumer tech and mobile apps
- Advanced proficiency in SQL, Python, and statistical modeling
- Proficiency with and excitement about using and building AI tools
- Experience partnering with early-stage or 0-to-1 product teams
- Familiarity with mobile-specific analytics and attribution tooling
- Applicants must be eligible to work in the US.
Compensation
- The actual base salary offered may vary depending on location, skills, qualifications, and experience.
Benefits
- Company Equity Package
- Medical, Dental, & Vision Insurance (PPO w/ HSA & FSA options)
- 11 Paid Holidays + Unlimited PTO & Sick Time
- 16 Weeks of Paid Parental Leave
- Creative Education Stipend
- Generous Commuter Allowance
- A self-starter mentality with the ability to thrive in ambiguity, eagerness to wear multiple hats, and passion for continuous learning
Company info
- Contribute to building strong data foundations as part of our awesome data science team
- We're building the world's first creative entertainment platform, where the entire world can feel the joy and fulfillment of making music.
Equal opportunity
- Equal Opportunity Employer.
This listing is sourced directly from Suno's careers page and normalized into a canonical job model.