Data Scientist at Discovery Limited

Key Purpose

The Data Science Lab is a highly specialised and expanding team that is dedicated to creating long-lasting, impactful solutions to meet essential business needs. Our primary objectives include:

• Developing and maintaining robust capabilities that will serve the business for years to come, with the goal of helping our members lead healthier lives.
• Risk management through behavioural science and intervention (next best action) design.
• Combining traditional data (e.g. wearable devices, web & app logs, health & life insurance claims) with novel data sources in new ways.
• Employing Large Language Models to elevate our servicing and coaching capabilities.
• Assisting with experimental design for product, rewards, marketing, communications, engagement, etc.
• Fostering a culture of experimentation through a test-learn-adapt approach, ensuring continual improvement and innovation.
• Advising global partner markets on how to customise and deploy locally built models.
• Individuals with two to five years’ experience (not required but advantageous) in the above areas will have the opportunity to work with cutting edge technology and advanced techniques to see their models used in real business applications. The innovative work environment means there are opportunities to shape new projects with a focus on helping insurance customers to lead healthier lives.

Areas of responsibility may include but not limited to

• Data analysis and modelling
• Extracting and analyzing clinical, lifestyle, and behavioral data to inform product design, risk management, customer interaction strategies for Personalised Health Pathways (PHP).
• Developing and maintaining predictive, value and habit models and algorithms to drive personalised recommendations.
• Developing data science models and A/B testing pipelines for deployment.
• Applying statistical techniques, behavioral science principles, and data science models to measure and optimise member engagement.
• Collaboration with Cross-functional Teams
• Working closely with Vitality, clinical, product, and research teams to ensure insights and modelling inputs/outputs align with product objectives and healthcare outcomes
• Collaborate with systems and engineering teams to integrate data science pipelines into downstream systems.
• Reporting and Insights
• Present insights and results to business stakeholders and senior executives, highlighting the impact of personalised health journeys on user engagement and long-term health improvements.

Personal Attributes and Skills  

• A creative and enthusiastic attitude to unearthing valuable insights and generating value for Discovery clients
• Ability to balance multiple priorities and to step back and see how analytics work fits into the wider business context
• Results driven, curious and able to work autonomously or within teams
• Good time and task management skills
• Ability to communicate results of analyses in a clear and effective manner
• Aligned to Discovery values and core purpose 

Education and Experience

• Honours, Master’s or PhD degree in either Data Science, Actuarial Science, Statistics, Operations Research, Computer Science or Applied Mathematics.
• Ability to formulate a clear problem statement, develop a plan for tackling it, and clearly communicate findings verbally, visually, and in writing.
• Demonstrable working experience in an analytics position, where the focus was on building and implementing machine learning models to solve business problems.
• Experience accessing and analysing data using language/tools/databases such as Python, R, SQL, etc.
• Experience using Gradient Boosting Machines, Random Forests, Neural Networks or similar algorithms.
• Good knowledge of Microsoft Office tools.

Advantageous:

• Some experience in working with big disparate sets of data and exposure to big data tools.
• The ideal candidate will possess a deep interest in the wellness and healthcare industries, particularly in leveraging behavioural science to promote healthier lifestyles, disease management and prevention. Additionally, they should demonstrate a strong understanding of strategic risk management principles and their application across the healthcare value chain.