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 data streaming technologies and event-driven architectures

Proven ability to continuously improve machine learning models through data monitoring, retraining, and adaptation to changing data patterns

Leading deployment expertise in managing the life cycle of AI and machine learning model from analytics sandboxes all the way to deploying machine learning models into production environments and effectively monitoring their performance

Leading ability to design, develop and maintain AI and machine learning systems, leveraging best practices in MLOps principles

Deep understanding of bias detection techniques to ensure ethical and unbiased machine learning models

Deep understanding of data & model governance, Bias detection and ethical AI

Demonstrate knowledge of data governance principles, including data quality, security, and compliance

Proven track record of designing, developing, and maintaining machine learning and AI applications, encompassing data preparation, model training, evaluation, and deployment

Strong knowledge of agile DataOps, MLOps and AIOps methodologies, enabling rapid development, testing, and deployment of machine learning solutions

Strong understanding of API Integration, and authentication for machine learning models and analytics applications

Stay current with industry AI, data science trends, vendors, tools, and other technology solutions to influence the architecture of Sun Life’s AI and Analytics applications

Strong ability to clearly communicate machine learning concepts and findings to non-technical stakeholders

Qualifications

Bachelor’s degree in a computing related discipline or equivalent industry experience is required.

7-10 years of industry experience is preferable with roles that demonstrate senior levels of software engineering and data analytics responsibilities.

Responsibilities

Design, develop, install, deploy, test, medium-scale analytics models and AI applications in support of business and client outcomes and objectives

Design and implement training pipelines for machine learning models

Build leading MLOps pipelines to deploy and update machine learning models and analytics applications

Build leading feature engineering pipelines extracting and preparing data for machine learning model performance

Train and evaluate models using diverse datasets and performance metrics, choosing and applying appropriate machine learning algorithms

Maintain and optimize model to improve accuracy and monitor reliability

Implement leading monitoring mechanisms to track model performance and identify issues

Detect and mitigate biases in machine learning models to promote fairness

Write leading specifications of machine learning models and processes for reproducibility and maintenance

Present insights and performance tuning recommendations from machine learning models to inform business decisions

Promote ethical principles and responsible AI guidelines for machine learning development

Integrate machine learning models into production environments

Develop and deploy APIs for model access and prediction requests

Leverage best practices and patterns in MLOps (machine learning operations) to ensure efficient and scalable model lifecycle management

Integrate leading AI service and machine learning models with existing applications and DevSecOps deployment pipelines

Embrace Agile development practices and employ SDLC processes on all development

Collaborate with data scientists practitioners, finance and business analysts, actuaries and engineers to achieve model objectives

Effectively mentor other engineers

Lead a "data-first" approach to problem solving and designing IT solutions

Job Category:

Advanced Analytics

Posting End Date:

12/06/2026