Data Science Manager, Consumer
New
R
RedditConsumer Technology
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
- PythonSQLA/B testingR
Requirements
- Advanced degree (masters or PhD) in a quantitative field such as statistics, mathematics, physics, economics, or operations research
- 10+ years of industry experience for masters holders
- 6+ years of industry experience for PhD holders
- Experience in driving product strategy and roadmaps through analytics, ideally for consumer technology products and marketplaces
- 2+ years of leadership experience as a people manager, leading a team of 5+ data scientists
- Expertise in causal inference, A/B testing experimentation, metric definition and governance, and product strategy within teams led
- Led initiatives to enable analytics self-serve for cross-functional teams
- Excellent communication skills to both technical and non-technical people
- Ability to understand nuanced complex concepts and systems, and explain them simply
- Proficient in SQL for hands-on analysis
- Proficient in Python/R for hands-on analysis
- Collaborative attitude and natural curiosity
- Strong evidence-backed product opinions
Responsibilities
- Lead a team of talented data scientists to drive and optimize user growth.
- Uncover insights and drive strategic initiatives.
- Collaborate closely with cross-functional partners (Product, Engineering, Design, Marketing) to build and improve systems that continuously drive user growth.
- Drive the adoption of strategic and tactical recommendations based on deep and hands-on experience with Reddit products and data skills.
- Serve as a thought-partner for product managers, engineering managers, and leadership, communicating and shaping the roadmap and strategy.
- Identify actionable and impactful insights through deep-dive analyses and analytics.
- Be proactively involved in all phases of product development: ideation, exploratory analysis, opportunity sizing, metrics design, offline modeling, experimentation, decision-making, and post-launch monitoring/measurements.
- Work on ETLs, reporting dashboards, and data aggregations needed for business tracking and/or ML model development.
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