Director, Data Scientist

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


  • 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

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