Data Scientist, Collaborative Data Services
Data Scientist, Collaborative Data Services
CBS BUSINESS UNIT:
New York, NY
CBS Interactive is the premier online content network for information and online operations of CBS Corporation as well as some of the top native digital brands in the entertainment industry. Our brands dive deep into the things people care about across entertainment, technology, news, games, business and sports. With over 1 billion users visiting our properties every quarter, we are a global top 10 web property and one of the largest premium content networks online.
Check us out on  The Muse,  Instagram and  YouTube for an inside look into 'Life At CBSi' through employee testimonials, office photos and company updates.
Interest in applying machine learning e.g, supervised methods like logistic regression, SVMs, random forests, deep learning & reinforcement learning, to existing products and services to improve their effectiveness and the overall user experience across our digital properties.
Leverage datasets like traffic/video/ consumption data from Adobe Analytics clickStream, purchase/ subscription data, audience data from syndicated audience measurement services (comScore/ Nielsen), and Ad revenue data from Doubleclick, to model, analyze and predict user behavior.
Utilize various statistical measures (mean, median, variance, etc.), distributions (uniform, normal, binomial, Poisson, etc.) and analysis methods (ANOVA, hypothesis testing, etc.) to build and validate models from observed data.
Data Scientist responsibilities include:
Build and implement production machine learning systems that improve/ solve business high-impact business problems
Maintain the health of machine learning systems, including speed, reliability, and performance
Develop internal machine learning frameworks and abstractions to facilitate common tasks such as training / testing, feature use / reuse / creation / storage, and deployment. - These abstractions are used by both machine learning engineers and data scientists/BI
Leverage impactful and innovative technology based on machine learning to improve existing products like video playlists, and create new products like implicit preferences-based related-story widgets, user-optimized content on the homepage, and smart onsite messaging tools to present users with the perfect offer at the perfect time
Use your mathematical, statistical and programmatic knowledge to search, analyze, and model in order to forecast the traffic and consumption of our digital properties and the sale of our subscription products
Read white papers, synthesize information, apply theoretical ML concepts to real-world business problems across our digital properties
Document, train, and mentor members of the BI team on ML concepts and principles, and how they can be applied to solve day-to-day business needs
Build a user-level data management platform that serves as the central data hub allowing machine learning software components to plug-in and segment users, predict future purchase behavior, identify users at risk of churning, and personalize their online experiences
Elevate the video asset recommendation engine for sports writers, so videos are driving longer engagement and increase in viewing loyalty
Use look-alike modeling derived from Adobe Analytics clickStream-sourced behavioral data to build larger audience segments from smaller segments in order to create targeted reach for advertisers
Create a true unique user count for both logged-in users and anonymous users, based on a device graph that accounts for multi-platform access, and pan-visit user behavior
Incorporate anomaly detection across KPIs, metrics and datasets to proactively find data issues, data load problems, and failures with minimal human supervision
MA/MS in Statistics/Data Science/Computer Science or related disciplines with specialization in data mining or machine learning techniques
Knowledge of both supervised and unsupervised machine learning techniques
Have full stack experience in data collection, aggregation, analysis, visualization, productionalization, and monitoring of data science products
Proficiency in big data modeling work: Hadoop, Pig, Scala, Spark
Proficiency in Python/R and associated machine learning packages is a must
Communicate concisely and persuasively with engineers and product managers
Strong detail-orientation with a penchant for data accuracy and good grammar
Must successfully pass a background check
Experience using project management tools like those from Atlassian (JIRA, Confluence)
Experience using Google Cloud Platform (BigQuery, ML Engine, and APIs)
Can fish for data: SQL, Pandas, MongoDB
Background in NLP or text mining techniques is a plus
Background in deep learning and Tensorflow is a plus
Equal Opportunity Employer Minorities/Women/Veterans/Disabled