Company DescriptionDropbox is a leading global collaboration platform that's transforming the way people work together, from the smallest business to the largest enterprise. With more than 500 million registered users across more than 180 countries, our mission is to design a more enlightened way of working. From our headquarters in San Francisco to eight dedicated Studios and a worldwide team of employees who choose where they work best, our Virtual First approach is leading the way into the future of work.
Team DescriptionOur Engineering team is working to simplify the way people work together. They’re building a family of products that handle over a billion files a day for people around the world. With our broad mission and massive scale, there are countless opportunities to make an impact.
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!
- 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
- 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
- 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
Benefits and Perks
- Generous company paid individual medical, dental, & vision insurance coverage
- 401k + company match
- Market competitive total compensation package
- Free Dropbox space for your friends and family
- Wellness Reimbursement
- Generous vacation policy
- 11 company paid holidays
- Volunteer time off
- Company sponsored tech talks (technology and other relevant professional topics)