Senior Data Scientist – Consumer Analytics – Content Creation Products (SF)

  • Company: LinkedIn
  • Posted: January 08, 2017
  • Reference ID: 1360369468
LinkedIn: 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. Consumer Analytics team: The Consumer Analytics team at LinkedIn works in close partnership with the Product team to identify opportunities to develop and enhance products and features. We use our rich member data to measure, understand and improve our products. Each data scientist on our team embeds within a product team and works closely with product managers, engineers, designers and a cross-functional team to drive LinkedIn's products.
Our Content Experience Content Creation product team focuses on driving LinkedIn's unique content ecosystem via creation of long form, short form and video content by members, Influencers and LinkedIn's unique global Editorial team. These products are core to LinkedIn's everyday use case, and the first content members interact with when they come to LinkedIn - join us in ensuring members create and get the news and insights they need as soon as they log on to LinkedIn! You'll be a part of the larger Consumer Analytics team, where 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 a Senior Data Scientist to join our team. You will be responsible for informing business and product strategy and decisions. Responsibilities: 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 experimentation platform Develop models and data-driven solutions that add material lift to principal performance metrics Provide technical guidance to more junior team members 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 your work, to evangelize data-driven product innovation
Basic Qualifications: BS/MS degree in a quantitative discipline: statistics, operations research, computer science, informatics, engineering, applied mathematics, economics, etc. 3+ years' industry experience working with large amounts of real data with SQL (Teradata, Oracle, or MySQL) and R, or other statistical packages 3+ years' industry experience providing analytical insights and business reports to product or business functions
Preferred Qualifications: Experience in Hadoop or other MapReduce paradigms and associated languages such as Pig, Hive, etc. 5+ years of experience programming in Java or Python and working with large datasets 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 Experience presenting insights to executive staff on a regular basis PhD in a quantitative discipline: statistics, operations research, computer science, informatics, engineering, applied mathematics, economics, etc.

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