Lead Data Science

**Where you would add value**

The role will be responsible to oversee data science team and be the technical head of a global workforce across a multi-country team. The objective of the role is to ensure timeous, robust and accurate research, conception, delivery and ongoing monitoring of data science products and frameworks. Effective communication between key stakeholders (actuarial, statistical, technology, terminology and data teams) is vital to ensure delivery.

**How you would make a difference**

Core responsibilities include:

- Guide and lead a multinational team to create and deliver on the vision of data science
- Responsibility for team and delivery through effective leadership and management
- Lead, design and recommend the overall data science architecture, technology and software design, etc.
- Guide on orchestration principals of data science and make recommendations for standard protocols/frameworks
- Technical input and guidance on best analytic approaches to all related technical problems
- RnD and lead development of new data science modules and features, updates to modules
- Lead the implementation of core components within new markets including rolling out of components, training/understanding of core components and validation with clients
- Lead the team in continued support of core components including updates, reworks and maintenance
- Contribute to key issues for discussions among senior technical leaders across the business
- Manage and grow a world class team by creating a sustainable operating model and talent pipeline
- Feedback to core analytic design and architecture team on possible product enhancements
- Communication across the organisation from staff to executive level for key delivery

Additionally, you will be required to contribute to the innovation and product creation process.

**What you need to be successful**

**Behavioural skills**
- Exceptional leadership and communication skills across a wide range of stakeholders
- Ability to work cohesively in a team environment while balancing multiple priorities
- High level of attention to detail, resilience, enthusiasm, energy and drive
- Positive, can-do attitude focused on continuous improvement
- Ability to take feedback and constructive criticism to drive improved delivery
- Rigorous ability to problem-solve and optimise environment
- Excellent management and co-ordination skills

**Technical understanding**

A deep understanding of the technical tools used in data science is required as the bases for development of analytical products, as such the following core understandings are required:

- Deep knowledge of end-to-end MLOp’s process with integrated CI/CD principals on both the model development and production environments
- Practical experience and knowledge of a range of AI and ML algorithms from general linear models to neural networks to reinforcement learning
- Practical experience in SQL, python, and advanced excel in the context of data science
- Knowledge of data science and solution architecture on the cloud -Azure cloud preferred
- Knowledge of cloud technologies such as data bricks, azure studio, data factory, etc.
- Understanding of data science in patient health management, provider profiling, healthcare reporting, and other key healthcare technologies etc. is beneficial but not required
- Understanding of data science in fraud, operations, lifestyle, behavioural environment is beneficial but not required

**Qualifications**
- A bachelor's degree in actuarial science, statistics, healthcare related, or similar
- A master's degree in a related field preferred
- Extensive experience in healthcare analytics

Instructions for interested applicants