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ViacomCBS Machine Learning Engineer in United States

REF#: 36385

CBS BUSINESS UNIT: CBS Interactive

JOB TYPE: Full-Time Staff

JOB SCHEDULE:

JOB LOCATION: USA

ABOUT US:

CBS Interactive, a division of ViacomCBS, is the world’s largest publisher of premium digital content and a perennial top 10 Internet company. CBS Interactive’s brands span popular categories like technology, entertainment, sports, news and gaming.

Properties include the websites, apps and streaming services of the CBS Television Network such as the CBS All Access subscription service, CBS News Digital platforms including the 24/7 digital news network CBSN, and CBS Sports Digital brands including the 24-hour streaming sports news network CBS Sports HQ, as well as digital-first properties in key content verticals, including CNET, ZDNet, TVGuide.com, GameSpot, Last.fm, Metacritic and Chowhound.

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DESCRIPTION:

Division Overview:

The Applied Machine Learning Group within CBS Interactive’s Advanced Technologies division focuses on deploying machine learning techniques to improve products.

Role Details:

Interest in applying machine learning e.g, supervised methods like 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, purchase/subscription data, and third-party data sources, to model, analyze and predict user behavior. Take ownership of machine learning products from conception through delivery and monitoring.

Your Day-to-Day:

  • Design and develop highly scalable and reliable machine learning solutions to improve user experience.

  • Overcome engineering challenges with deploying machine learning solutions at scale

  • Investigate new ways machine learning can improve products and determine viability of solutions

  • Keep up with machine learning research and commercial product offerings across a wide variety of machine learning fields including NLP, recommendations, image and video.

  • Participate in design and code reviews

  • Effective communication is critical as this is a distributed team

Key Projects:

  • Pioneer new product ideas for how machine learning can improve products and take ownership from inception through implementation.

  • Deliver recommendations across CBS platforms served to millions of customers daily.

  • Work on state-of-the-art video solutions applying machine learning to both live streaming video and VOD.

  • Curate our content platform to design and develop sophisticated products to collect, transform and enrich data in a fast, scalable and reliable way for ourselves and our customers.

#LI-JG1

QUALIFICATIONS:

What you bring to the team:

You have -

  • MA/MS in Statistics/Data Science/Computer Science or related quantitative 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 Python and associated machine learning packages is a must (TensorFlow preferred), experience with a Python web framework (Django/Flask) is preferred

  • Communicate concisely and persuasively with engineers and product managers

You might also have -

  • Experience with Tensorflow Extended (TFX), KubeFlow and related tools

  • Experience using project management tools like those from Atlassian (JIRA, Confluence)

  • Familiarity with version control systems (Git and Bitbucket)

  • Experience using Google Cloud Platform (BigQuery, ML Engine, and APIs)

  • Background in NLP or text mining techniques is a plus

  • Background in deep learning is a plus

  • Familiarity with Kubernetes and cloud deployments

  • Experience with elasticsearch

  • Desire to contribute to the open-source community (i.e., blogs, source code, etc)

EEO STATEMENT:

Equal Opportunity Employer Minorities/Women/Veterans/Disabled

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