As part of the Peacock Decision Sciences team, the VP Recommendation Science, will provide strategic leadership to a team of data scientists in the design and execution of advanced analytical solutions using advanced data science methodologies including collaborative filtering, deep learning, reinforcement learning, on-line modeling etc. and lead collaboration with business owners and engineering team to build a state-of-the-art real-time video streaming service.
• Build and strategically lead a high-performance team of data scientists.
• Lead the team in the development of a recommendation system modeling and experimentation framework.
• Work with business stakeholders to define priorities, approaches and business requirements for the analytical solutions.
• Manage multiple priorities across business verticals and machine learning lifecycle projects.
• Lead work with engineering teams to define data science driven requirement and solutions for major initiatives and opportunities of the streaming service functionality.
• Drive innovation of the statistical and machine learning methodologies and tools used by the team. Lead improvements in machine learning lifecycle infrastructure.
• Drive a data science culture that inspires and motivates the team to succeed.
• Advanced (Master or PhD) degree with specialization in Statistics, Computer Science, Data Science, Economics, Mathematics, Operations Research or another quantitative field or equivalent.
• Minimum 12 years of combined experience in advanced analytics in industry or research.
• Minimum 7 years of departmental and people leadership.
• Experience with commercial recommender systems or a lead role in an advanced research recommender system project.
• Working experience with deep learning, particularly in the areas different form the computer vision. Strong experience with deep learning using TensorFlow.
• Experience implementing scalable, distributed, and highly available systems using Google Could Platform.
• Experience with Google AI Platform/Vertex AI, Kubeflow and Airflow.
• Experience in data processing using SQL and PySpark.
• Good understanding of algorithmic complexity of model training and testing, particularly for real-time and near real-time models.
• Advanced experience in media analytics and application of data science to the content streaming and TV industry.
• Good understanding of reinforcement learning algorithms.
• 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 with large-scale video assets.
• Ability to build trust across the organization and socialize solutions and identified data insights.
• Proficiency in Python. Java or Scala is a plus.
Jobcode: Reference SBJ-g3x6nq-3-80-3-192-42 in your application.