Principal ML Analytics Engineer

You are as unique as your background, experience and point of view. Here, you’ll be encouraged, empowered and challenged to be your best self. You'll work with dynamic colleagues - experts in their fields - who are eager to share their knowledge with you. Your leaders will inspire and help you reach your potential and soar to new heights. Every day, you'll have new and exciting opportunities to make life brighter for our Clients - who are at the heart of everything we do. Discover how you can make a difference in the lives of individuals, families and communities around the world.

Job Description:

Design, development and deployment of medium-scale analytics models and AI applications that are high-quality, innovative, efficient, creative, scalable, adaptable, dynamic, fully performing and comply with all standards and best practices in support of business and Client outcomes and objectives. Manage the scalability and economics of the machine learning (ML) models and the integration of those models with Sun Life applications and infrastructure. Work independently and collaborate with the data scientists, Cloud infrastructure provisioning and administration teams on the specification and provisioning of appropriate analytics infrastructure and platforms required for an initiative. Exhibits leading understanding of analytics engineering concepts such as: data modeling, testing, governance, MLOps, AIOps, data technologies and focused on learning more about these domains and continuously honing their craft. Realize business value from data utilizing data as a strategic asset across business functions and business groups. Ability to focus on task at hand, lead execution and delivery of prescribed plan with ability to be flexible and think under pressure. Highly motivated team player with a strong desire to learn, grow, and continuously improve.

Preferred skills

• Leading understanding of artificial intelligence, generative AI, and data science concepts and principles, including machine learning algorithms, statistical methods, and data mining technique.
• Proven proficiency in various platforms managing the life cycle of machine learning models, practices and tools involved in managing the machine learning lifecycle (MLOps), such as model versioning, monitoring, and deployment
• Leading knowledge of data science platforms, big data technologies. Understanding of AI and ML Service Platforms like Dataiku, Amazon SageMaker or Azure Machine Learning.
• Leading skills in mathematical / data science programming languages, particularly Python, R, and SQL. Ability to utilize these languages for data exploration, manipulation, and analysis
• Proficiency in database management using relational and non-relational databases, Cloud data warehousing platforms with multi-clustered distributed data architecture highly scalable, and elastic compute
• Strong understanding of da