VP, Data Sciences
WWE
Stamford, CTThis was removed by the employer on 6/9/2021 7:20:00 AM PST
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Full Time Job
The VP, Data Sciences will be responsible for building and executing on the Data Science roadmap for WWE. Working with the head of Data Technology, this specialist will drive data insights and decision making across all LOBs by building the next generation of data products, using machine learning algorithms and data science and following reproducible, auditable, collaborative development practices. Some illustrative examples include building topic models to get insight into fan conversation (using Twitter, YouTube et al), developing forecasting models for TV, Digital ratings and ecommerce sales.
They will work closely with the business teams at WWE and the data analytics business partners to identify use cases, convert them into analytical problems and build solutions and/or products fit for purpose. They will lead the Data Science and Advanced analytics COEs consisting of a team of analysts, data scientists and machine learning engineers. They will leverage WWE's data management platform and work closely with our tech teams responsible for managing various parts of the data stack. Our environment is dynamic, fast-paced, and lots of fun.
Key Responsibilities
• Build a strategic vision for the role Data science, Machine learning and advanced analytics can play in enabling various WWE lines of business
• Lead, grow and retain a strong team of data scientists, analysts and machine learning engineers
• Ensure the team has the right tools, processes and agile principles in place to deliver work that is on time and to specification
• Partner with multiple business stakeholders and cross-functional teams to design, develop and execute multiple data science projects and/or build out of Machine learning products with the urgency appropriated for the business objectives
• Advance existing initiatives and open opportunities to pursue new and previously unexplored research topics across a wide variety of domains
• Use wealth of available data stored in variety of internal and external data stores to deliver important insight to drive the decision-making process
• Focus on automation and utilizing technology to provide repeatable solutions; Drive optimization and efficiency initiatives to support reduced cycle time
• Supervise the creation and maintenance of required documentation for all owned deliverables
Qualifications:
• 7+ years of experience leading data and engineering organizations, preferably in the capacity of data science and business intelligence
• 5+ years of hands-on, technical experience with data analytics, data science and/or engineering
• Master's degree / Ph.D. in mathematics, applied statistics, information technology, or a related field or equivalent experience
• Expert in developing supervised and unsupervised machine learning algorithms (regression, decision trees/random forest, neural networks, feature selection/reduction, clustering, parameter tuning, etc.). Advanced knowledge in model evaluation, tuning and performance, operationalization and scalability of scientific techniques and establishing decision strategies.
• Practical experience with ML platforms such as Tensorflow/Keras, PyTorch, etc.
• Proficient understanding of fundamental AI and ML techniques; e.g., A*, regularization Expertise in one or more specialized areas; e.g., deep learning (DL), reinforcement learning (RL), planning, information representation and retrieval, graphs, multi-agent systems (MAS), computational game theory, natural language processing (NLP)
• Experience in evaluating and making decisions around the use of new or existing tools for a project
• Proficient in Python and atleast 1 of Spark, Scala, R, SQL, C++ or MatLab
• Experience leading teams that adopt DevOps & MLOps
• Experience managing and ensuring accuracy of numerous, operationalized models in production; Three to five successfully launched ML projects would be ideal
• Experience speaking in large and high visibility forums; demonstrable experience building relationships and communicating complex topics simply to the organization
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