Data Scientist at Rogers Communications in Toronto, ON

Data Scientists will be primarily responsible interpret and solve deep business problems by developing predictive models and analysis that make the best use of the wealth of Customer, Network and Channel information available across Rogers business units, channels and care and marketing platforms. Data scientists will collaborate with business primes from Wireless and Residential business to develop range of predictive models using advance Machine Learning and AI techniques and make business recommendations to:

• Manage customer churn
• Acquire and retain customers
• Build Likelihood-To-Recommend (LTR/NPS) clusters or models
• Increase revenue through cross sell and up sell products/services
• Increase campaign effectiveness

Overall Role Requirements:

• Serve as a subject matter expert in Data Science and Data Engineering technology and related processes.
• Work with engagement leads, program owners, and with leaders across divisions that can influence our Backlog and Roadmap.
• Understand business issues and decompose them into measurable data or modeling requirements.
• Analyze data from different sources and provide key insights throughout the strategy, design, development and deployment stages.
• Build predictive models leveraging advanced ML/DL techniques, tools and analytics frameworks to expose actionable insights in order to support Rogers businesses, manage customer base and improve relevant KPIs & performance metrics.
• Perform model tuning and frequent training to maintain proper health of predictive models by ingesting newer data sources.
• Monitor the model performance and make ongoing corrections by executing extensive model testing strategies.
• Prepare comprehensive model development and deployment documentation.
• Use advanced data mining techniques and perform data discovery to uncover the hidden patterns in data and translate learnings into a business actionable outcome.
• Develop and maintain an advanced knowledge of Rogers business and IT methodologies and apply this knowledge to project work.
• Deliver, track and articulate model values to the program owners.

Skills/Qualifications:

• Bachelor or Master degree in a technology/analytical field such as Computer Science, Management of Information Systems, Mathematics, Statistics, Machine Learning/AI, Engineering, or other relevant technology field.
• A minimum of five years of relevant professional experience in an Agile environment with excellent understanding of the underlying Statistical, Machine Learning theory and Predictive Modeling Lifecycle.
• Deep expertise in developing and maintaining the predictive models using advance ML/DL techniques. Proficiency in building Supervised learning models (classification and regression) – tree based (Random Forest, Stochastic Gradient Boosting and eXtreme Gradient Boosting, Decision Trees, Extra Trees, Regularized Greedy Forests), Generalized Linear Models, Discriminant models (LDA, MDA, FDA and QDA)], and Unsupervised learning models (Isolation Forest, Clustering algorithms).
• Excellent programming knowledge in any of the languages (SAS , Phyton , R, Spark/Scala).
• Expertise in data extraction, manipulation, feature engineering and validations.
• Experience in building Deep neural networks (MLP, CNN, RNN) and use of AI/Deep Learning frameworks like MXNet, Caffe 2, Tensorflow, Theano, CNTK, and Keras is an added advantage.
• Experience in working with any of industry standard analytical/ML modern ML platforms (like SAS Viya, Cloudera, Sage Maker Azure ML).
• Experience in managing and leading complex projects and multiple projects simultaneously, including (but not limited to) building descriptive, predictive and prescriptive solutions and deploying these solutions in the cloud and/or on premise systems.
• Strong organizational skills and the ability to manage multiple tasks simultaneously.
• Exceptional interpersonal, persuasion, and communication skills in order to communicate strategic and technical ideas to internal audiences to both inform and solicit buy-in from the end user community.