At Gnip, we believe the value of social data is unlimited. Data Stories is how we bring this belief to life by showcasing how social data is used. This week we’re interviewing data scientist Mohammad Shahangian of Pinterest about how the data science team works at Pinterest, surprising uses of Pinterest and data science as a career path. You can follow him on Pinterest at pinterest.com/mshahang.
1. What do you see is your role as the data scientist for Pinterest?
The company’s focus is on helping millions of people discover things they love and get inspiration to go do those things in their life. For me, that means analyzing the rich data that is created by the millions of people interacting with billions of pins from across the web each day. I evaluate this data and provide insights that make data actionable. My team also prototypes and validates ideas, performs deep analysis and builds tools that allow us to answer our most frequent questions in seconds. We work with every team to answer Pinterest’s biggest questions and ensure that each decision positively impacts Pinners over the long term.
For example, we take a business question like “How should our web, tablet and phone experiences differ?” and present the results as insights like, “Many users use the mobile apps in the morning and again at night, but prefer the website during the day” and “Users prefer to use mobile apps to casually discover new content, whereas they use the web to curate and organize content.” We then work with the design and product teams to build features around these insights and measure their impact.
2. What are some of your favorite ways that people use Pinterest that people wouldn’t expect?
What makes Pinterest unique is that it’s a tool and the users really define its use cases. For me, Pinterest was really helpful when I was planning my wedding and it made perfect sense to use as collaborative office shopping list. I would have never thought to use it as a tool for:
A collection of Stop signs from around the world
Daily Grommet gets their community to collaborate on a board to see things they want to sell
Vintage Driving - a collaborative board where users pin their favorite vintage cars:
GE Badass machines featuring GE tech
Madewell’s Rainbow board
Michelle Obama’s MyPlate Recipes encourages health eating
Stunning virtual collections of minerals and shipwrecks
The “365 Days of Pinterest” challenge. She made a Pinterest project every day for a year!
Sammy Sosa awesomeness
Sony shows off their technology with food pictures shot with a Sony Camera
Pantone announces the color of the year
The National Pork Board
3. What category do you see as the most viral on Pinterest?
DIY and recipes pins generally go viral year round. Around the holidays, holiday-themed content across all categories tends to get the most traction.
4. How has data science added value to Pinterest?
We have this internal value we refer to as “knit.” It means that we have an open, curious culture where everyone in different disciplines—from engineering and design to marketing to community—works together. Data science is at the core of that. The search, recommendations and spam teams apply data science to improve the quality of content we put in front of Pinners. This is only a subset of how we apply data though; most of the decisions we make at Pinterest are actually backed by data.
Data is a universal language that teams across the company use to collaborate and make decisions. Each team has a set of performance metrics, and we hold a weekly meeting to understand the impact that each area is having on company-wide metrics. As data scientists we do more than just analyze data, we create rich data sources that we make available to other teams so they can do their own analysis. More than half of Pinterest employees run MapReduce jobs via Hive. Our metrics dashboards are accessible to everyone and our core metrics are emailed daily to the entire team. We also share our data studies and insights with the whole team.
We also use data just for fun. During our weekly happy hour, we share a weekly Data Fun Fact with the team. We present the fact in the form of a multiple choice question and have the team vote on the answer. For example, we asked, “How many days before Valentine’s day does the query ‘Valentine’s day ideas’ increase the most: 1, 3, 5 or 7 days?” (Hint for the curious reader: two*three/two).
5. What do you think someone should know before becoming a data scientist at a major web company like Pinterest?
I would say go for it! If you are hungry to extract value from real world data, you’re really going to enjoy it. I know that for a lot of really talented people in academia the only thing standing between them and the opportunity to solve a really interesting problem is the lack of rich data. My experience at Pinterest has been the exact opposite. Our team can’t grow fast enough to tap into a world of valuable insights that are sitting dormant within billions of records somewhere in the cloud.