Data Scientist, Capacity Engineering - Location Flexible

Role Description

Are you a critical thinker who can help solve ambiguous problems facing infrastructure powering 600M+users?  Are you equally at home explaining your analyses and project recommendations with executives as you would be discussing the technical merits of your solution with wider audiences? If that sounds like you, you might be a great fit for our team! We are looking a rock star Data Scientist who can jump in and make an impact quickly. This is a unique opportunity were you get to work on both cost and revenue side of the business! You have to think critically and strategically about Dropbox’s infrastructure as a technology, a business and as an operation. For example, you should be comfortable setting up and analyzing A/B tests for our experimentation platform and understand how it impacts to revenue. You also get to drive impact on the cost strategy by building forecasting models to predict infrastructure growth and user growth. As this team continues to grow there's also an opportunity to help shape and guide its expansion through mentorship and advocacy across the company. We hope you'll join us!

Responsibilities

Revenue Strategy

• Build cool models to find actionable insights around customer experience metrics through funnels, cohort analyses, long-term trends, user segmentation, ML models, and more
• Create simple, trustworthy data pipelines and automate reporting of surface key metrics
• Employ experimentation tools to enable targeting of offers and experiences based on user attributes

Cost Strategy 

• Build scalable and pragmatic statistical forecasting models to predict how 600M+ users impact Dropbox infrastructure needs (Storage/CPU/Memory etc)
• Develop cost models to predict 3 year infrastructure gross margins and give insights on cost metrics and metric movers to engineering leadership
• Build and deliver impactful presentations that tell a persuasive story with convictive insights, not just data

Requirements

• Degree in Math, Physics, Statistics, Economics, Computer Science, or other quantitative field
• 4+ years experience doing causal inference, quantitative analyses  and modeling for a technology company (Experience in product/infrastructure data science is a bonus) 
• Fluency in SQL and statistical programming (e.g., R or python)
• Experience in ETL methodology for performing Data Profiling, Data Migration, Extraction Transformation, and Loading 
• A solid understanding of statistical analysis, experimentation, and the common pitfalls of data analysis
• Good understanding of basic ML techniques just as regressions, tree based models, clustering techniques and SVMs
• Self-starter: You recognize gaps and drive projects with minimal guidance and focus on making a large impact
• Strong communicator: You effectively synthesize, visualize, and communicate your ideas to others
• Critical thinker: You are thoughtful, self-aware, and use available evidence to make decisions
• Collaborative: You work effectively with others and win credibility quickly