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Director, Data Scientist
Sales & User Ops Team | San Francisco, CA
Team Description
Our Sales team shares Dropbox for Business with enterprises around the world, helping them understand the power Dropbox has to offer teams at scale. We're a collaborative and empathetic Sales team, focused on understanding what businesses need to work better together.
Dropbox is looking to become the industry leader in Data Science-driven sales. The Revenue Data Science team will deliver analytics and tools to drive revenue by combining the rich internal data across 500M+ Dropbox users and external data on buying patterns of prospects and customers.
Roles and Responsibilities
- Use machine learning to research, design, implement, and validate leading-edge propensity models to analyze large scale network collaboration (e.g., 500M+ users) and micro-segment prospects and customers alike. Examples include:
- Account prioritization: segmenting and scoring account lists using propensity-to-buy models that combine Dropbox and external data sources
- Account prospecting: generating scaleable analytics & models that link Dropbox data with company characteristics to create sales pipeline
- Opportunity scoring: using Bayesian modeling to generate regularly-updating sales opportunity scores
- Conceptualize, design and build data-fueled insights to help Dropbox improve analytics for prospects and customers. Examples include:
- Benchmarking: Develop comparative indexes that measure how companies compare to industry peers in key performance and usage metrics
- Predictive modeling: Build lead scoring algorithms to prioritize and time when account team should engage with which customers
- Recommendation engine: Use real-time analytics to recommend ways in which customers can maximize adoption and usage of Dropbox
Requirements:
- PhD or Master’s Degree in computer science, applied statistics, data mining, machine learning, or a related quantitative discipline
- 8+ years of experience delivering world-class data science outcomes
- Ability to solve complex analytical problems using quantitative approaches with a unique blend of analytical, mathematical and technical skills
- Highly detailed-oriented and exceptional organizational and follow-through skills a must
- Strong data-oriented scripting (e.g. SQL) and statistical programming (e.g., R or python)
- Excellent judgment and creative problem solving skills
- Entrepreneurial team player who can multitask
- Exceptional written, oral, interpersonal, and presentation skills and the ability to effectively interface with senior management and staff