Data Scientist

Sun Life

. Translate business goals into analytical problems .

Posted 9/24/2026full-timeToronto • CanadaJunior💰 CA$65,000 - CA$105,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and implementing data science solutions using Python, SQL, and AWS services, particularly SageMaker. Capable of translating business goals into analytical problems and effectively communicating insights to stakeholders.

Highest-signal resume keywords

Python ProficiencyAWS SageMaker ExperienceMachine Learning Model EvaluationData Transformation and ManipulationStrong Communication Skills

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Statistical TechniquesHypothesis TestingCausal InferenceSupervised Machine LearningUnsupervised Machine LearningSQL Query WritingPySpark Query WritingFeature EngineeringModel TrainingModel Evaluation

Soft Skills

Problem-SolvingAnalytical SkillsAttention to DetailStakeholder EngagementProject Management

Tools & Technologies

AWS GlueAWS LambdaAWS Step FunctionsData WarehousingBig Data TechnologiesAPIsMicroservices ArchitectureDockerCI/CD ConceptsGenAI Frameworks

Certifications & Qualifications

Bachelor's Degree in StatisticsMaster's Degree in MathematicsMaster's Degree in Computer ScienceMaster's Degree in Engineering

Industry Keywords

Data ScienceMachine LearningContinuous ImprovementInnovationData Quality

Tech Stack

Tools & technologies

AWSCloudDockerMicroservicesPySparkPythonSQL

About the role

Key responsibilities & impact

Translate business goals into analytical problems
Identify optimal algorithms, statistical techniques, and traditional machine learning methods for business problems
Work in cross-functional teams to develop machine learning and data science products
Apply descriptive, predictive, and machine learning methods from design through implementation
Perform 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 including AWS Glue, Glue Catalog, Glue Data Quality, and AWS Step Functions
Break down data science development milestones into actionable goals, activities, and work plans
Create and maintain technical design artifacts covering application functionality, data models, interfaces, and integrations
Engage and negotiate with stakeholders
Make business recommendations and present findings effectively to stakeholders at multiple levels
Champion continuous improvement and foster innovation within the analytics community

Requirements

What you’ll need

Bachelor's or Master's degree in Statistics, Mathematics, Computer Science, Engineering, or related field, or equivalent experience
1–2 years of experience developing and implementing data science solutions
Proficient in Python for data science and application development
Experience writing complex SQL and PySpark queries
Solid understanding of hypothesis testing, causal inference, and model evaluation metrics
Experience with AWS, particularly SageMaker for training and deploying ML models
Proficiency in supervised and unsupervised machine learning models
Experience with data transformation, manipulation, and structured and unstructured data
Strong understanding of APIs, microservices architecture, and cloud-native development
Exceptional communication, storytelling, and insights communication abilities
Ability to manage multiple projects with changing deadlines and priorities
Strong problem-solving and analytical skills with attention to detail
Reliability Status Clearance required before starting employment
Must satisfactorily complete applicable background checks before the start date and during employment
Preferred: hands-on experience with GenAI frameworks and LLM APIs, including AI bots/agents with reasoning and tool-use capabilities
Preferred: understanding of RAG techniques, prompt engineering, and fine-tuning methodologies
Preferred: familiarity with vector databases and embedding models
Preferred: experience with Docker and CI/CD concepts

Benefits

Comp & perks

Wellness programs supporting mental, physical, and financial health
Variety of career paths and networking opportunities
Hybrid work with choice of working from home or in the office based on business and Client needs
Employee participation in discretionary incentive plans, subject to individual and company performance
Mental health-focused workplace support and positive work culture