• BS or higher in Computer Science, Statistics, Mathematics, Physics, a related quantitative field, or equivalent experience
• 7+ years of industry data science experience
• Experience effectively communicating & collaborating with non-technical stakeholders
• A deep understanding of the “why” behind your work, not just the how
• Strong Python skills in data analysis using pandas and Jupyter Notebook
• Experience writing and optimizing complex SQL queries on large data sets
• A strong background in inferential statistics, especially in an experimentation setting (hypothesis testing, power analysis, experimental design)
• Experience with different data analytics and BI tools such as Looker, Tableau, PowerBI, or Snowflake
• Experience in big data technologies like Spark

Desirable

• As a Staff Data Scientist at Grindr, you will be working closely with product managers, product designers, and engineers to devise insightful metrics, design experiments, apply your statistical expertise to guide key decisions, and build visualizations
• You will have the opportunity to work on models that are deployed to production and improve quality of life for our millions of users
• You will also be an informal ambassador for the Data Science team to the rest of the organization, educating a diverse group on the many unintuitive aspects of data science
• You will be a member of the data organization at Grindr, which integrates data scientists, analysts, and engineers into a single cohesive and collaborative team
• This is a rare opportunity to learn, teach, and have an outsized impact alongside some of the top minds in the field
• Extract actionable insights from broad, open-ended questions
• Design and evaluate experiments to measure the impact of product changes
• Analyze data from across the product to uncover the root causes of metric movements
• Communicate results to cross-functional stakeholders to inform product decisions
• Develop tools to scale and automate analyses, improving productivity across the company
• Provide mentorship and guidance to team members + identify and recommend best practices
• Apply an engineering mindset: minimize complexity while maximizing utility & maintainability
• Be part of building our future state, devising ML solutions to improve recommendations, detect spam, and better serve our users