Associate, Content QA
New York, NY
Meet Peacock, NBCUniversal's new, wildly entertaining streaming service that combines timeless shows and movies with timely news, sports and pop-culture.
We're growing our team of smart, hungry, and upbeat doers who crave the chance to build something new at the epicenter of content, tech, and culture. We need fearless leaders and pop-culture fiends who work hard and fan hard. Creative problem-solvers who just so happen to be the reigning champs at Parks & Rec trivia night. So if this sounds like you, join our flock. And we promise, we won't put your stapler in Jell-O.
Data Scientist, Peacock
Are you ready to go beyond classic predictive analytics to the new areas like deep learning and reinforcement learning? Do you want to be a part of the team that builds and advanced recommender engine, active leaning platform, personalized solutions for marketing and advertisement?
Join us if you would like to be a part of an exciting initiative.
As part of the Direct-to-Consumer Decision Sciences team, the Data Scientist will be responsible for creating analytical solutions for one or more verticals of the video streaming service including but not limited to the recommender system, automated marketing, personalized advertisement, commerce and revenue optimization, customer journey and CRM solutions.
In this role, the Data Scientist will use advanced data science methodologies including collaborative filtering, deep learning, reinforcement learning, on-line modeling etc. and work close with teammates and engineers to build a state-of-the-art real-time video streaming service.
Responsibilities include, but are not limited to:
• Build recommendation engines and other analytical models using statistical, machine learning and data mining methodologies.
• Apply cleansing, discretization, imputation, selection, generalization etc. to create high quality features for the modeling process.
• Use big data, relational and non-relational data sources to access data at the appropriate level of granularity for the needs of specific analytical projects. Maintains up to date knowledge of the relevant data set structures, access levels and ownership information.
• Collaborate with engineering teams to build real-time and automated batch implementations of the data science solutions.
• Work on improving the codebase and machine learning lifecycle infrastructure. Have active participation in all aspects of the team work.
• Bachelor's or advanced degree with specialization in Statistics, Computer Science, Data Science, Economics, Mathematics, Operations Research or another quantitative field or equivalent.
• 1+ years of combined experience in advanced analytics in the industry or research.
• Hands on experience in statistical methods and machine learning with special emphasis on the advanced algorithms like neural networks, SVM, random forests, bagging, gradient boosting machines, k-means++, deep learning or reinforcement learning.
• Experience implementing scalable, distributed, and highly available systems using Google Cloud or similar cloud platform.
• Experience with data visualization tools and techniques
• Strong knowledge of statistical and programming languages such as R, Python, Julia, Java, Scala or similar.
• Strong knowledge of deep learning frameworks such as Tensorflow or PyTorch.
• Experience in data processing including SQL and PySpark.
• Working experience with commercial recommender systems or a lead role in advanced research recommender system project.
• Experience with reinforcement learning based systems.
• Working experience with deep learning, particularly in the areas such as NLP, adversarial learning, autoencoders, wide & deep learning, and computer vision
• Experience with multi-billion record datasets.
• Knowledge of enterprise-level digital analytics platforms (e.g. Adobe Analytics, Google Analytics, etc.)
• Experience with television ratings and digital measurement tools (Nielsen, Rentrak, ComScore etc.)
• Experience building streaming data pipelines using Kafka, Spark or Flink
• Experience with large-scale video assets
• Team oriented and collaborative approach with a demonstrated aptitude and willingness to learn new methods and tools
• Exhibit a bias for getting the job done
• Pride and ownership in your work and confident representation of your team to other parts of NBCUniversal
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