Role Summary

Within Sun Life, the Data Science Chapter comprises of savvy and intellectually curious professionals who are on a mission to transform how we apply data and analytics to support Sun Life becoming client centric. An integral part of the Data and Analytics organization, the Data Science Chapter supports Canadian business units in their journey to leverage data and analytics as a foundational pillar in delivering business value.

Reporting to the Data Science Manager as a Data Scientist you will focus on supporting Canadian business units in accelerating the growth and application of advanced analytics in driving value. The data scientist will leverage practical experience in applying varied data science techniques & offering advice/inputs to help with the design, development and implementation of analytics use cases.

Sun Life views success in this role in demonstration of these key attributes:

Fierce curiosity. You are drawn to discovering and leveraging data and taking on challenging business problems. An inquisitive mind. Driven to ask questions to help lead projects to business value and not being afraid that the innovation attempts can and will lead to failing. A passion for solving problems. Technical skills in both data and computer science. There are 3 core technical skills we look for: in-depth coding knowledge of an analytical tool(s) (i.e., Python); data science techniques and concepts; working with structured data and unstructured data. Thirst for learning. You are a data scientist who is constantly updating their knowledge of data science state-of-the-art.

Key Responsibilities:

Data Science and Machine Learning

• Translate business goals into analytical problems; Identify optimal algorithms, statistical techniques, traditional ML suitable for the business problem at hand.
• Work in cross-functional teams to develop ML/data science products
• Apply best-in-breed data science techniques including descriptive, predictive, and machine learning methods from design to implementation
• Focus on feature engineering, model training and model evaluation
• Use AWS services including SageMaker, Lambda and other AI/ML services
• Work with data warehousing, pipelines, and big data technologies such as AWS Glue for ETL, Glue Catalog, Glue Data Quality, and AWS Step Functions.

Role Summary

Within Sun Life, the Data Science Chapter comprises of savvy and intellectually curious professionals who are on a mission to transform how we apply data and analytics to support Sun Life becoming client centric. An integral part of the Data and Analytics organization, the Data Science Chapter supports Canadian business units in their journey to leverage data and analytics as a foundational pillar in delivering business value.

Reporting to the Data Science Manager as a Data Scientist you will focus on supporting Canadian business units in accelerating the growth and application of advanced analytics in driving value. The data scientist will leverage practical experience in applying varied data science techniques & offering advice/inputs to help with the design, development and implementation of analytics use cases.

Sun Life views success in this role in demonstration of these key attributes:

Fierce curiosity. You are drawn to discovering and leveraging data and taking on challenging business problems. An inquisitive mind. Driven to ask questions to help lead projects to business value and not being afraid that the innovation attempts can and will lead to failing. A passion for solving problems. Technical skills in both data and computer science. There are 3 core technical skills we look for: in-depth coding knowledge of an analytical tool(s) (i.e., Python); data science techniques and concepts; working with structured data and unstructured data. Thirst for learning. You are a data scientist who is constantly updating their knowledge of data science state-of-the-art.

Key Responsibilities:

Data Science and Machine Learning

• Translate business goals into analytical problems; Identify optimal algorithms, statistical techniques, traditional ML suitable for the business problem at hand.
• Work in cross-functional teams to develop ML/data science products
• Apply best-in-breed data science techniques including descriptive, predictive, and machine learning methods from design to implementation
• Focus on feature engineering, model training and model evaluation
• Use AWS services including SageMaker, Lambda and other AI/ML services
• Work with data warehousing, pipelines, and big data technologies such as AWS Glue for ETL, Glue Catalog, Glue Data Quality, and AWS Step Functions.