Lead Data Scientist

Job Description

Responsibilities
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. ​
Lead 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 the world-class international team of smart, hungry and fearless professionals who are working at the cutting edge of technology and science at the epicenter of content, technology and culture.

Position Overview:
As part of the Direct-to-Consumer Decision Sciences team, the Lead Data Scientist will be responsible for creating analytical solutions for one or more verticals of NBCU's video streaming service including, but not limited to, the recommender system, automated marketing, personalized advertisement, commerce and revenue optimization systems, customer journey and CRM solutions.

In this role, the Lead Data Scientist will use advanced data science methodologies including collaborative filtering, deep learning, reinforcement learning, on-line modeling etc. and work closely with business owners, teammates and engineers to build a state-of-the-art real-time video streaming service.

Responsibilities include, but are not limited to:
• Lead development of recommendation system using advanced machine learning techniques such as deep learning, graph modeling, collaborative filter, and reinforcement learning
• Lead research initiatives into state-of-the-art methodologies that will enhance current models and power future personalization features
• Define procedures for cleansing, discretization, imputation, selection, generalization etc. to create high quality features for the modeling process.
• Work with business stakeholders to define business requirements including KPI and acceptance criteria.
• 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. Maintain up to date knowledge of the relevant data set structures and participate in defining necessary upgrades and modifications.
• Collaborate with software and data architects in building real-time and automated batch machine learning pipelines that include data preprocessing, validation, model training, serving, testing, and measurement.
• Drive work on improving the codebase and machine learning lifecycle infrastructure

Qualifications/Requirements
• Advanced (Master or PhD) degree with specialization in Statistics, Computer Science, Data Science, Economics, Mathematics, Operations Research or another quantitative field or equivalent.
• 3+ years of combined experience in advanced analytics in industry or research.
• 2+ years of experience using deep learning frameworks. Tensorflow or Pytorch preferred
• Experience in leading small teams or/and being a lead data scientist on large commercial projects.
• Deep knowledge of statistical methods and machine learning with special emphasis on algorithms like deep learning, reinforcement learning, Bayesian inference, causal modeling, graph modeling, and self-supervised learning.
• Experience implementing scalable, distributed, and highly available systems using Google Cloud or AWS.
• Experience with data visualization tools and techniques such as Tableau, Looker, or matplotlib
• Understanding of algorithmic complexity of model training and testing, particularly for real-time and near real-time models.
• Proficient in at least one statistical (R, Python) and one general programming (Julia, Java, Scala or similar) languages.
• Strong skills in data processing using SQL and Spark.

Desired Characteristics
• Working experience with commercial recommender systems or a lead role in an 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 and leading projects that span the disciplines of data science and data engineering
• 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
• Pride and ownership in your work and confident representation of your team to other parts of NBCUniversal

Jobcode: Reference SBJ-rve7x7-18-218-127-141-42 in your application.