Talent Pool - Senior Data Scientist

Discovery Health

Senior Data Scientist

The Group DS Lab is growing and positions are available. The lab applies predictive analytics, machine learning, big data and operations research skills, to run and to support key projects for the Group and for the individual Discovery business units. We work across clinical, wellness, financial, sales, operational, people and behavioural theme areas, using modern analytics tools on terabytes of structured and unstructured data within a big data architecture. We are also mandated to find opportunities to use new data sets and in areas not typically accustomed to using data science.

The Data Science Lab is a highly specialised and expanding team that tackles challenges in the health, life, and short-term insurance businesses, as well projects that cut across the whole of the Discovery Group. We are looking for individuals with 2-5 years of experience, for projects related to:

- risk management through behavioural science and intervention (next best action) design

-combining traditional data (eg: wearable device, web & app logs, health & life insurance claims) with novel data sources in new ways

-assisting with experimental design for product, rewards, marketing, communications, engagement etc

-advising partner markets on how to customise and deploy locally built models

They 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.

Master’s or PhD degree in either Data Science, Actuarial Science, Statistics, Operations Research, Computer Science, Applied Mathematics or Engineering fields.

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 healthcare industry, particularly in leveraging behavioral science to promote disease management and prevention. Additionally, they should demonstrate a strong understanding of strategic risk management principles and their application across the healthcare value chain.

Identify and build appropriate models to predict risk, sales and savings

Present data insights and model findings in a way that provides actionable insights for business stakeholders and senior executives

Mining and visualising large structured and unstructured datasets throughout the businesses to inform product design, risk management, customer interaction strategies, etc.

Following model implementations through to business adoption

Monitoring model performance and using feedback for improvement

Improving processes and data collections where opportunities arise

Running scientific experiments to evaluate different models in a reproducible way

Produce analytical work that is customer, business and staff focused