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.
At Sun Life, we're driven by our Purpose: helping our Clients achieve lifetime financial security and live healthier lives. Our values shape how we work: caring, authentic, bold, inspiring, and impactful.
When you join Sun Life, you'll work with passionate colleagues and empowering leaders who support your growth and celebrate your contributions, so you can make a meaningful difference in our Clients' lives.
Discover how you can make a difference in the lives of individuals, families and communities around the world.
Job Description:
The Associate Director, Data Scientist is a senior, business‑facing data science role, sitting within Data & Analytics, responsible for driving insurance business outcomes through AI, Advanced analytics and machine learning, leveraging our data foundation.
This business-side data science leadership role focuses on transforming client, policy, interaction, and behavioral data into actionable insights, next-best actions, and decision intelligence for the Life & Health and Wealth & Pension businesses.
The role combines hands‑on AI / data science capability (Python, AWS), strong insurance business acumen, and AI leadership, including responsible use of machine learning and GenAI to problem solving and stakeholder engagement. It is accountable for analytics and AI value realization from C360, not for platform delivery or data governance.
Key Responsibilities
Insurance Analytics & AI Use Case Leadership
- Lead insurance‑specific analytics and AI use cases built on Client 360, including:
- Client segmentation and profiling
- Cross‑sell, up‑sell, and next‑best‑action models for advisors
- Client lifecycle, retention, and persistency analytics
- Personalisation client engagement and targeting
- Frame analytics and AI initiatives around clear insurance business outcomes (growth, advisor productivity, client retention)
- Define KPIs and track measurable commercial and operational impact
Hypothesis‑Driven Data Science & Experimentation (AWS / Python)
- Own a hypothesis‑driven analytics approach, translating business questions into testable hypotheses
- Design and execute experiments and test‑and‑control mechanisms, including:
- Champion / challenger models
- A/B and multivariate testing
- Controlled rollouts for analytics‑driven decisions
- Ensure experiments are statistically sound, interpretable, and aligned with insurance business constraints
- Quantify incremental impact and causal uplift, distinguishing signal from noise
- Embed closed‑loop learning to continuously refine models, rules, and decision logic
Hands‑on AI / Data Science Delivery
- Design, build, and deploy Python‑based data science and AI models
- Perform feature engineering using client, policy, interaction, and behavioural data
- Partner with technology and engineering teams to productionize analytics and experiments on AWS
- Ensure models and experiments are robust, scalable, explainable, and suitable for regulated insurance environments
- Monitor model and experiment performance over time and drive continuous improvement
Qualifications
Core Skills (Required)
- Strong hands‑on experience in Python for data science and machine learning
- Proven experience delivering analytics solutions on AWS
- Strong SQL and experience working with large client‑ or policy‑level datasets
- Track record of moving analytics from prototype to production
Experience & Domain
- 8–12+ years in data science, advanced analytics, or decision science
- Experience in insurance or financial services is strongly preferred
- Good understanding of insurance business levels is strongly preferred
- Personalisation and targeted engagement
- Client lifecycle and persistence
- Distribution effectiveness and advisor enablement
- Familiarity with Client 360, CRM, or enterprise data platforms is a strong advantage
Leadership & Mindset
- A growth mindset to problem solve and curiosity to explore problems to be solved.
- Comfortable operating at the intersection of business, analytics, and technology
- Prior experience in business consulting and analytics advisory is a plus.
Job Category:
Advanced AnalyticsPosting End Date:
27/08/2026
