Strategic Analytics Intern

Strategic Analytics

Strategic Analytics is a team within Pacific Life Re's R&D function. We:

• Act as a centre of excellence for data science and analytics initiatives (e.g., predictive underwriting, data mining, machine learning-enabled analysis) across the Asia/Australia business
• Deliver innovative solutions for the life insurance market, including underwriting rules engine design, portfolio risk management, product pricing and development, and sales processes
• Operate as a global co-hub for data science initiatives alongside the Strategic Analytics team in London, with a focus on Asia/Australia market priorities
• Collaborate globally with Strategic Analytics colleagues across regions to deliver market-relevant solutions and share best practices
• Develop partnerships with clients, academic bodies, and third parties, contributing innovative product offerings and research to the Asia/Australia insurance market
• Enable better co-ordination across PLRe's research and analytics resources in Europe, Toronto, Asia, and Australia, given geography and time zones

The Role

As a divisional co-hub for Strategic Analytics, the intern will support the collection, validation, and analysis of Pacific Life Re's unique insurance data sources across global offices, working with Strategic Analytics colleagues based in London and Australia. The role will also contribute to projects for Asia and other markets in which Pacific Life Re operates, developing innovative insurance solutions utilising insights from predictive analytics.

Duties

• Support data scientists on analytics projects for Pacific Life Re and UnderwriteMe clients, covering both innovative data utilisation and predictive modelling
• Identify and work on opportunities to improve or automate existing end-to-end processes for analysing data
• Actively participate in research-related initiatives and develop innovative tools for both internal and external use

Main Project Examples

Project scope will be tailored to the intern's strengths, interests, and business priorities.

• Explore insurance and business datasets to identify patterns, trends, and opportunities using statistical, analytical, and machine learning techniques. This may include exploratory research into emerging approaches such as clustering, segmentation, anomaly detection, and other advanced analytical methods
• End-to-end predictive underwriting machine learning pipeline (core analytics work): Support the full modelling lifecycle, including data validation, cleaning, feature engineering, model development, testing/validation, and performance monitoring
• Pipeline improvements and maintenance: Help improve repeatability and quality by enhancing pipelines (e.g., reusable code, automated checks, documentation) and assist with model maintenance (e.g., refresh with new data, investigate performance drift)
• GenAI exploration (where suitable): Explore and prototype ways to incorporate GenAI into the ML workflow (e.g., monitoring, reporting, triage, and recommendations for model updates)
• Cross-region collaboration: Work with stakeholders across multiple regions to understand varying business needs, data contexts, and operational constraints

The exact nature of responsibilities and project assignments will depend on the successful candidate's skills, interests, academic background, and business requirements at the time of the internship.

Duration and Working Arrangement

• Approximately six months in total
• Flexible arrangement combining full-time work during university vacation periods and part-time work during academic semesters
• Typical term-time commitment is expected to be approximately 3+ days per week, subject to mutual agreement
• Exact schedule and duration will be agreed with the successful candidate

Qualifications & Experience

Essential:

• Current second-year or penultimate-year student studying a bachelor's degree in statistics, mathematics, actuarial science, or a related discipline
• Strong mathematical background and experience relevant to developing statistical and machine learning models, including the associated processes in data preparation and data visualisation
• Strong knowledge and experience in relevant tools and programming languages (Python/R, SQL, Tableau, etc.)
• Sufficient understanding in data engineering and computer science to assist in different aspects of data science projects
• Keen to work collaboratively with AI-enabled tools, with a willingness to learn how to use them effectively and responsibly to support research, analysis, problem-solving, and development
• Good communication and presentation skills, particularly in explaining complex issues in clear terms
• Enthusiastic, with a keen drive and focus