Staff Data Scientist, Grindr

• This role is ideal for someone who is technically strong, curious, and eager to deepen their product thinking and analytical leadership
• BS or higher in Computer Science, Statistics, Mathematics, Physics, a related quantitative field, or equivalent experience
• 6+ years of industry data science experience, specifically in Trust & Safety or similar work
• Strong communication skills and comfort working with cross-functional partners
• Proficiency in Python for data analysis (e.g., pandas, Jupyter)
• Strong SQL skills and experience working with large-scale data
• Solid foundation in statistics and experimental design
• Familiarity with BI tools like Looker, Tableau, or PowerBI
• Some exposure to big data tools like Spark is a plus

Desirable

• As a Staff Data Scientist (Ecosystem Health) at Grindr, you’ll partner closely with product managers, designers, and engineers to define meaningful metrics, design and analyze experiments, and uncover insights that inform product development for the Ecosystem Health initiatives
• Our Ecosystem Health Team focuses on increasing real-people retention by building a safer, more authentic community through proactive harm prevention, strong identity trust, and empowering users to own their safety experience, while seamlessly meeting evolving regulatory expectations
• This is a high-impact role at the intersection of AI/ML, fraud prevention, risk management, and trust & safety, driving strategies and product innovation that ensure authenticity, reduce harmful behavior, and build user confidence worldwide
• You’ll be a valued member of Grindr’s centralized Data organization, which brings together data scientists, data engineers, and ML/AI engineers into a collaborative team
• You’ll have the opportunity to grow your impact while learning from peers across disciplines
• Define and track key product metrics in collaboration with cross-functional partners
• Design, run, and analyze A/B tests to evaluate the impact of new features
• Perform data deep dives to explain trends and uncover actionable insights
• Communicate findings clearly to stakeholders and influence product decisions
• Build dashboards and tools to help teams monitor metrics and make data-informed decisions
• Contribute to the development of best practices within the Data Science team
• Explore and prototype ML applications to improve product quality (e.g., recommendations, spam detection)