At Pocket we love what we do, but more importantly we are building something that millions of people love, too. Pocket has become essential to how people discover and consume content on any device.
If you are passionate about using technology to improve people's lives, we believe Pocket has challenges in front of us that will excite you. We're a small team and that means lots of opportunity to own things from start to finish that have tremendous impact on large numbers of people, across many different devices and platforms.
We have data. Lots of it. We've been using it for quite some time to guide the choices we make in building the best possible Pocket for our users. Now we're looking to take the data team to the next level by adding a data scientist with deep experience in predictive analysis and machine learning.
What you’ll do:
- Embody the "voice of the data" - advising our product, engineering and business operations teams and providing input on everything we build, test and analyze
- Help teams define meaningful metrics, frameworks and visualizations to track the success of our product and business work.
- Design and implement algorithms that power user-facing features used by millions of people every day.
- Build a world-class team and shape the future of data at Pocket.
- Work within our office in the heart of downtown San Francisco.
What you already do:
- Have 2+ years working on a well-respected data science team. Consumer-facing product experience a plus.
- Use statistics, data mining and modeling to extract useful data and insights from complex data sets.
- Possess strong communication skills and an ability to approach problems in a structured way and distill complex issues into actionable insights.
- Dream of solving complex analytics problems and applying large-scale distributed computing tools (Hadoop, Hive, etc).
- Have experience with scripting languages (such as Python), the ability to extract own data using SQL, and a belief in reproducible analysis.
To apply, send us your resume, along with:
- Analytics-based projects to which you have contributed significantly.
