Numeris
Media
SeniorManager,DataScience
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optimal for Manager candidates.
“Senior Manager, Data Science at Numeris. Skills: Data science, Data engineering, ML/AI, Business intelligence”
Industry & Context.
Full Job Description
## Description Numeris is Canada’s most trusted and authoritative source for broadcast measurement and consumer behaviour data. As well, the industry leading intelligence provider to broadcasters, advertisers, and agencies. We have been recognized for over 75 years as providing the gold standard in audience intelligence. We have great people who do exceptional work. We take pride in building an environment of continuous learning and career advancement opportunities. Our teams are collaborative, curious, and dedicated to creating quality experiences. We love what we do, enjoy each other’s company, and support initiatives to give back to our communities. We are looking for people to help us shape the future of Numeris, and have a lot of fun along the way. The Senior Manager, Data Science is responsible for the design and validation of advanced analytics methodologies at Numeris. This role leads the review and application of machine learning, statistical approaches and hybrid methodologies, integrates diverse data sources and ensures the accuracy, reliability and research rigour of both syndicated and ad-hoc outputs. The Senior Manager provides technical leadership and people management for the Data Science team, while working closely with other teams within Research as well as Product, Technology and Operations stakeholders. ## What you will do Be responsible for the quality and statistical research rigor of Numeris data products including developing and reviewing weighting, fusion, calibration, attribution and other statistical processes as well as the accuracy and reliability of data outputs. Manage the Data Science team in order to provide recommendations for the use of statistical, Data Science and AI methodologies, the continuous improvement of current analytical data processes and procedures, and the expansion of value added to data products. Oversee the application and review of data mining, data integration and hybrid methodologies. Assess the integrity of
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