About the role
- [Domain] Deep expertise in building the right systems for data analysis, modeling, visualization and prediction, to land optimal business outcomes.
- [Technical Leadership] Expert problem solver, able to balance technical requirements, with human elements, business constraints, and broader project context, to effectively drive trade-offs amidst high ambiguity. Assume hands-on leadership, especially when helping teams set their long-term vision and resolve complex problems through iterative execution.
- [Project management]: Proficient in planning projects, managing requirements across key stakeholders, and ensuring ethical data practices.
- [Collaboration]: Working with the executive team, and key cross-functional stakeholders to identify opportunities and implement models to influence the org effectively.
- [Communication]: Presenting findings to stakeholders and communicating complex insights to non-technical audiences.
Minimum requirements
- ~10 years of data science/quantitative modeling experience to solve business and product problems; proficiency in SQL and a computing language such as Python or R
- Strong knowledge and hands-on experience in several of the following areas: machine learning, statistics, optimization, product analytics, causal inference, and/or experimentation
- Demonstrated experience of leading organization-wide initiatives spanning multiple teams, or leveraging deep domain and business expertise to influence tech roadmap planning and execution
- Ability to communicate results clearly with a focus on driving impact
- Demonstrated ability to effectively collaborate across multiple teams and stakeholders to drive business outcomes
- Demonstrated ability to balance execution and velocity with research, statistical depth, and scalable design.
- A builder's mindset with a willingness to question assumptions and conventional wisdom
- May be required to participate in on-call rotation
Preferred Qualifications
- Experience designing, running, and analyzing complex experiments or leveraging causal inference designs
- Experience working in Growth on problems like attribution, channel performance optimization, CAC optimization and experimentation.
- Experience with distributed tools such as Spark, Hadoop, etc.
- A PhD or MS in a quantitative field (e.g., Statistics, Engineering, Mathematics, Economics, Quantitative Finance, Sciences, Operations Research)Experience developing and deploying metric frameworks
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