A Data Scientist at Sun Life Financial plays a pivotal role in transforming the company’s vast data assets into actionable strategic insights. Operating within a leading international financial services organization, you will design, develop, and deploy advanced analytical models that directly influence underwriting, risk management, product design, and client engagement. The work is highly collaborative, bridging the gap between complex quantitative engineering and core business strategies across insurance, wealth management, and digital health solutions.

The impact of this role is substantial, as your models will help optimize pricing structures, detect fraudulent claims, and personalize financial planning experiences for millions of clients globally. By leveraging machine learning, predictive modeling, and natural language processing, you will help modernize legacy financial processes and drive digital transformation. This makes the position both intellectually challenging and highly rewarding, as you balance technical innovation with the rigorous compliance and security standards of a major financial institution.

Candidates entering this role can expect to work on high-impact initiatives alongside cross-functional teams of data engineers, product managers, and business stakeholders. Success requires not only deep technical expertise in statistical modeling and machine learning but also strong business acumen and the ability to translate complex data findings into clear, strategic recommendations for non-technical leaders.

The questions you will encounter during the Sun Life Financial hiring process are designed to evaluate your technical depth, business problem-solving capabilities, and communication skills. The following categories represent common patterns observed in actual interview experiences, reflecting a mix of behavioral reflection, machine learning theory, and practical business case studies.

Behavioral & Experience

These questions assess your past project execution, collaboration style, and how you handle challenges in a professional environment.

• Walk me through a complex data science project you led from conception to deployment. What were the key challenges, and how did you overcome them?
• Describe a situation where you had to explain a highly complex machine learning model to a non-technical business stakeholder. How did you structure your communication?

To succeed in the Sun Life Financial interview process, you must demonstrate a balanced skill set that spans technical mastery, structured problem-solving, and strong interpersonal communication. Your preparation should focus on showing how your technical work drives tangible business value.

Role-related knowledge – You must demonstrate a deep understanding of machine learning algorithms, statistical modeling, and data manipulation techniques. Be prepared to defend your choice of algorithms, feature engineering methods, and validation strategies using concrete examples from your past work.

Problem-solving ability – Interviewers will evaluate how you approach ambiguous, unstructured business challenges. You should focus on establishing a clear framework, defining measurable target variables, identifying data limitations, and designing robust validation schemes that align with business objectives.

Communication & Influence – As a data scientist, you must translate complex statistical concepts into clear, actionable business strategies. You will be evaluated on your ability to articulate the "why" behind your technical decisions and build alignment with cross-functional stakeholders who may not have a technical background.

Culture fit & Values – Sun Life Financial highly values collaboration, integrity, client-centricity, and continuous improvement. Show how you navigate team dynamics, handle constructive feedback, and maintain high ethical standards when working with sensitive financial and personal data.

The interview process for a Data Scientist at Sun Life Financial is structured to evaluate both your technical execution and your strategic business thinking. While exact stages may vary slightly depending on the specific business unit and seniority level, the process generally spans two to three rounds and is characterized by a respectful, collaborative atmosphere.

The journey begins with an initial HR screen, followed by technical discussions with senior leaders and a practical case study challenge. Interviewers are typically polite, supportive, and deeply interested in the practical applications of your past work.

The timeline above outlines the standard progression from your initial application to the final decision. Candidates should use this timeline to pace their preparation, ensuring they focus on behavioral and foundational technical concepts early on, before diving deep into case study frameworks and model design methodologies for the final stages. While the process is structured, the exact timeline can vary depending on the hiring team's urgency and whether an on-site final round is required.

To excel in the core technical stages of the Sun Life Financial interview process, you must understand the specific competencies being evaluated in each major round.

Past Project Execution & Technical Depth

During the technical interviews, Directors and senior team members will dive deep into your resume, focusing heavily on the end-to-end lifecycle of projects you have personally delivered. They want to understand your individual contributions, decision-making processes, and technical rigor.

Be ready to go over:

• Algorithm selection – Why you chose specific models (e.g., gradient boosting, neural networks, linear models) over alternative approaches.
• Feature engineering – How you transformed raw, messy data into high-value predictive signals, and how you handled missing values or outliers.
• Model validation – Your strategy for preventing overfitting, including cross-validation techniques and how you aligned offline metrics with online business KPIs.
• Advanced concepts (less common) – Hyperparameter optimization frameworks, handling high-cardinality categorical variables, and implementing model explainability techniques like SHAP or LIME.

Example questions or scenarios:

• "Walk me through the feature engineering process for the predictive model you highlighted in your resume. How did you validate that these features were robust?"
• "Describe a scenario where your model's offline validation metrics were highly positive, but its performance degraded significantly post-deployment. How did you diagnose the issue?"

Machine Learning Model Design (Case Study)

The case study round is designed to simulate a real-world business challenge at Sun Life Financial. You will be asked to conceptualize and outline the architecture of a machine learning model from scratch, demonstrating your ability to build scalable, practical solutions.

`

`

Be ready to go over:

• Problem formulation – Translating a broad business goal into a specific machine learning objective (e.g., classification, regression, recommendation).
• Data acquisition & leakage – Identifying required data sources and proactively preventing data leakage during the training phase.
• Deployment & monitoring – Outlining how the model will be integrated into production systems and how its performance will be monitored over time.
• Advanced concepts (less common) – Designing real-time vs. batch inference pipelines, managing feedback loops in recommendation systems, and setting up automated model retraining schedules.

Example questions or scenarios:

• "How would you design an automated system to flag potentially fraudulent health insurance claims in real-time? Walk me through the data inputs, model choice, and validation strategy."
• "We want to predict which existing clients are most likely to purchase an additional wealth management product. Describe how you would build, evaluate, and deploy this model."

Business Analytics & Translation

This evaluation area focuses on your ability to generate business value from data. Sun Life Financial looks for data scientists who do not just build models in a vacuum, but who can actively partner with business units to drive strategic decisions.

Be ready to go over:

• KPI alignment – Ensuring your model's success metrics (e.g., precision, recall, RMSE) directly translate to business success metrics (e.g., cost savings, revenue generation, customer retention).
• Stakeholder communication – Articulating complex technical trade-offs in simple, impact-oriented language.
• A/B testing & experimentation – Designing robust experiments to measure the real-world lift of your models compared to existing baselines.
• Advanced concepts (less common) – Designing multi-armed bandit experiments, calculating financial risk exposure under model uncertainty, and conducting causal inference analysis.

Example questions or scenarios:

• "If a business stakeholder insists on using an easily interpretable but less accurate model over a highly complex, accurate black-box model, how would you evaluate and present the trade-offs?"
• "How would you design an A/B test to measure the impact of a new automated underwriting algorithm compared to our current manual process?"

As a Data Scientist at Sun Life Financial, your day-to-day activities will be dynamic, blending deep technical development with strategic business collaboration. You will be responsible for driving the entire analytical lifecycle, from initial data exploration to production deployment and performance monitoring.

Your primary responsibilities will include:

• Collaborating closely with business analysts, product managers, and executive stakeholders to identify high-impact opportunities where machine learning and advanced analytics can optimize business outcomes.
• Designing, building, and validating predictive models and machine learning algorithms using Python, R, and SQL, ensuring they meet rigorous performance and compliance standards.
• Partnering with data engineering teams to design scalable data pipelines, clean unstructured data sources, and ensure high-quality inputs for your models.
• Translating complex quantitative findings into clear, compelling narratives and visual presentations for non-technical business leaders to drive strategic decision-making.
• Monitoring, maintaining, and iteratively improving deployed models to ensure they remain accurate, reliable, and secure in a production environment.
• Keeping abreast of emerging data science methodologies, tools, and industry trends to continuously elevate the team's analytical capabilities.