Data Science Manager, User Growth
US remote-friendlyFull-TimeManager
Salary217000 - 303900 USD per year
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Job Details
- Experience
- 10+ years for masters holders, 6+ years for PhD holders
- Required Skills
- PythonSQLETLA/B testingR
Requirements
- Advanced degree (masters or PhD) in a quantitative field such as: statistics, mathematics, physics, economics, or operations research
- For masters holders, 10+ years of industry experience
- For PhD holders, 6+ years of industry experience
- Experience in driving product strategy and roadmaps through analytics, ideally for consumer technology products and marketplaces
- Built, led and developed high performing product analytics teams before: ideally 2+ years of leadership experience as a people manager, leading a team of 5+ data scientists.
- Teams led had expertise in causal inference, A/B testing experimentation, metric definition and governance, and product strategy
- Led initiatives to enable analytics self serve for cross-functional teams
- Excels at communicating to both technical and non-technical people
- Ability to understand nuanced complex concepts and systems, and explain them simply.
- Proficient in SQL and Python/R for hands-on analysis
Responsibilities
- Lead a team of talented data scientists to drive and optimize user growth by uncovering insights and driving strategic initiatives.
- Collaborate closely with cross-functional partners (Product, Engineering, Design, Marketing) to help us build and improve the systems that continuously drive our user growth.
- Drive the adoption of strategic and tactical recommendations based on deep and hands-on experience with Reddit products and data skills, to maximize our value to Reddit consumers.
- Serve as a thought-partner for product managers, engineering managers and leadership from your respective product domain, communicating and shaping the roadmap and strategy for Reddit by identifying actionable and impactful insights through deep-dive analyses and analytics insights.
- Be proactively involved in all phases of product development, including but not limited to, ideation, exploratory analysis, opportunity sizing, metrics design, offline modeling, experimentation and decision-making, post-launch monitoring/measurements etc.
- Maintain a keen interest in the collection and quality of underlying data (experiment design and analysis, data deep dive), along with working on ETLs, reporting dashboards and data aggregations needed for business tracking and/or ML model development.
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