Manager, Cinema Audience Analytics
This job posting is no longer active
Location: Cineplex Media
City: Toronto, Ontario
Cineplex is one of Canada's leading entertainment companies and operates numerous businesses including theatrical exhibition, food service, amusement gaming (Player One Amusement Group), alternative programming (Cineplex Events), traditional and digital media (Cineplex Media and Cineplex Digital Media), out of home leisure and entertainment (The Rec Room), eSports (World Gaming), and home entertainment content (CineplexStore.com). Cineplex is also a joint venture partner in SCENE - Canada's largest entertainment loyalty program.
We are proudly a Canadian company with approximately 13,000 employees in Canada and the US, and we offer competitive compensation, incentive and benefits programs. To learn more about Cineplex please visit our website at www.cineplex.com.
The Analytics Department at our Home Office in Toronto is currently recruiting for the position of Manager, Cinema Audience Analytics
reporting to the Director, Data Science & Analytics. The Analytics Department is a Centre of Excellence for data analysis that supports the enterprise with self-serve analytics visualizations and applications, algorithm development and guidance on strategy for analytics and data science. This newly created position will be the primary analytics team liaison for Cineplex Media Sales and Account Management staff and therefore will be based out of our Cineplex Media office located at 102 Atlantic Avenue, Toronto, ON.
The Manager, Cinema Audience Analytics is responsible for planning, developing and optimizing analytics workflows focused on audience projections and demand forecasting. The successful candidate will be accountable for execution of analytics projects (data acquisition, modelling, testing, validation, deployment, ongoing maintenance) using datasets from disparate systems including point of sale, SCENE loyalty, traffic cameras, interactive gaming, CRM and Rentrak. Responsibilities
- Develop audience projection and demand forecasting models using multivariate statistical and machine learning techniques and methods.
- Monitor the business and technical performance of deployed models, plan and execute recommended enhancements for improved performance.
- Document and communicate business & technical data requirements for analytics model development.
- Document technical data requirements, conduct user acceptance testing and validate data preparation for datasets prepared by DBA team.
- Communicate project plans and status to Senior Leadership at Cineplex Media.
- Manage and maintain project planning documentation.
- Provide support and guidance to Cineplex Media Sales and Account Management staff.
- 5-7 years hands on training or experience working on analytics models using multivariate statistical and machine learning techniques and methods.
- 5-7 years of experience working with relational data sources.
- Degree or diploma in a related field (i.e.: Data Science, Analytics, Statistics, Industrial Engineering, Operations Research, Management Science, Mathematics)
- Must be resourceful, self-motivated and pay close attention to detail.
- Aptitude to read and write SQL.
- Excellent oral and written communication skills.
- Experience working with survey weighting would be an asset.
- Experience working with transactional point of sale and loyalty data would be an asset.
- Experience with advanced Microsoft Business Intelligence and Data Science tools would be an asset (i.e.: PowerBI, Azure ML, SSAS Data Mining, SQL Server Management Studio).
- Experience with the Alteryx Advanced Analytics platform would be an asset.
- Proficiency in the R programming language would be an asset.
Interested applicants please apply today.While we appreciate all interest, only those candidates selected for an interview will be contacted. As part of Cineplex Entertainment's standard recruitment process, suitable candidate(s) will be required to undergo pre-employment screening as a condition of employment or promotion.No Agency Calls Please