| Job Mode : | |
| Published On : | 2008-10-25 |
| Last Application Date : | 0 days remaining (2009-00-00) |
| Category : | Other (Other) |
| Pay Rate : | Negotiable |
| Location : | San Francisco |
| City : | San Francisco |
| Country : | USA |
| Job Posted By: | Job Search Engine |
| Jobs Publisher's Website: | |
Quantitative Product Analyst at Slide in San Francisco, CA
Want to be a part of the next big internet success story?
Want to be a tech pioneer alongside artists, athletes, musicians, and talent from around the globe?
Then you want to be at Slide.
About Slide:
We are the #1 developer of applications on social networks.
* Our applications, like Slideshow, FunSpace, SuperPoke! and Top Friends, are as technically deep and sophisticated as they are popular.
* Our 170 million users make us one of the top 10 web properties in the world; that's serious scale.
* Slide was launched by Max Levchin, who co-founded and sold PayPal for $1.5 billion.
Slide is a data driven company and we are looking for a Quantitative Product Analyst with exceptionally strong quantitative and analytical skills to perform detailed quantitative analyses and use them to guide product and business development.
Responsibilities:
* Conduct deep data driven analyses that answer business critical questions
* Perform both regular and ad-hoc analyses using both Python and MySQL to manipulate large amounts of data
* Work closely with product managers to ensure that all key metrics are being tracked with each product release
* Gather, track and analyze key performance metrics to provide explanations for significant changes and drive overall business growth
Qualifications:
* 3-5 years experience in a relevant field such as quantitative market research, data-mining or product management in a high-growth tech company.
* The drive to work in a fast-paced, start-up environment
* A strong interest in online social networks and network/user dynamics
* Superior intellectual, quantitative, and analytical abilities
* A degree or similar experience in a quantitative field is preferred (statistics, engineering, computer science, etc.)