Data Science Manager - Content Creation, Consumer Product Analytics San Francisco

  • Company: LinkedIn
  • Posted: December 17, 2016
  • Reference ID: 1866919954
LinkedIn was built to help professionals achieve more in their careers, and every day millions of people use our products to stay informed, stay connected, discover opportunities and gain insights. Our global reach means we get to make a direct impact on the world's workforce in ways no other company can. We're much more than a digital resume - we transform lives through innovative products and technology. Searching for your dream job? At LinkedIn, we strive to help our employees find passion and purpose. Join us in changing the way the world works.
LinkedIn's Content Experience Creation product team designs, creates, and established LinkedIn's publishing platform - where users can create long and short form items to share - be it text, video, images, or all of the above. Creating this content helps members establish their brand, while enabling their networks to stay informed. The team also includes an amazing global team of LinkedIn Editors, who create original content and curate content created by Influential LinkedIn members.
This team is looking for a Data Science Manager to lead a stellar team of data scientists who are working on providing our members with a great marketplace for content. Join us in making LinkedIn's publishing offerings immensely useful and compelling to the world's professionals!!
And as part of the larger LinkedIn Analytics team, you'll have the opportunity to work with some of the best data people anywhere in an environment that truly values data-driven decisions.
We are looking for an Analytics leader to lead the Content Creation Product Analytics team. You will be responsible for partnering with the product team leaders to ensure decisions are being made via best use of our data in creating and executing a business and product strategy. You will be responsible for growing, mentoring, and leading the analytics team members, for prioritizing projects across the team, and more broadly evangelizing data across LinkedIn.
You will be leading and guiding a team of data scientists whose day to day responsibilities include:
Extract and analyze LinkedIn data to derive actionable insights
Formulate success metrics for completely novel products, socializing them and creating dashboards/reports to monitor them
Design and analyze experiments to test new product ideas and convert the results into actionable product recommendations
As an end consumer of the data, determine the tracking necessary to enable analytics of our products and features by working closely with product and engineering partners
Enable others in the organization to utilize your work by onboarding new metrics into our self-serve data system and the experimentation platform
Develop models and data-driven solutions that add material lift to principal performance metrics
Exploratory data analysis with the goal of product ideation, prototyping, and developing new and innovative features that can drive additional value for/engagement from our members
Communicate the results of work, to evangelize data-driven product innovation
Basic Qualifications:
BS degree in a quantitative discipline: statistics, operations research, computer science, informatics, engineering, applied mathematics, economics, etc.
4+ years' industry experience working with large amounts of real data with SQL (Teradata, Oracle, or MySQL) and R, or other statistical package
4+ years' industry experience providing analytical insights and business reports to product or business functions
Preferred Qualifications:
MS degree/PhD in a quantitative discipline: statistics, operations research, computer science, informatics, engineering, applied mathematics, economics, etc.
Experience in Hadoop or other MapReduce paradigms and associated languages such as Pig, Sawzall, etc.
5+ years of experience programming in Java or Python and working with large datasets
Experience presenting insights to executive staff on a regular basis
Expertise in applied statistics and in at least one statistical software package, preferably R
Proficiency in SQL and in a Unix/Linux environment for automating processes with shell scripting
Advanced skills in Java/C++
Ability to communicate findings clearly to both technical and non-technical audiences
Ability to translate business objectives into actionable analyses

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