Data Scientist

**Introduction**

Through our client-facing brands Metropolitan and Momentum, with Multiply (wellness and rewards programme), and our other specialist brands, including Guardrisk and Eris Property Group, the group enables business and people from all walks of life to achieve their financial goals and life aspirations. We help people grow their savings, protect what matters to them and invest for the future. We help companies and organisations care for and reward their employees and members. Through our own network of advisers or via independent brokers and utilising new platforms Momentum Metropolitan provides practical financial solutions for people, communities and businesses.

**Role Purpose**
- Create and deliver data-driven solutions that add business value through the use of statistical models, machine learning algorithms, data mining and visualisation techniques.**Requirements**:

- Degree in Mathematics, Economics, Statistics, Engineering, Computer Science or other quantitative field
- Post graduate degree (preferably a masters or PhD) in a quantitative discipline that includes significant exposure to mathematics (linear algebra and calculus), statistics and computer science (desirable)
5-8 years’ data analysis experience within an insurance environment
- Extensive experience with at least one programming language (e.g. Python), business analytics software (e.g. SAS) statistical package (e.g. R)
- Experience in database language (e.g. SQL)
- Deep theoretical understanding of statistical methods and machine learning techniques
- Understanding of Artificial Intelligence solutions and techniques
Formulating business problems to enable statistical modelling
- Selecting the right statistical tools and techniques for the job
- Experience translating statistical findings into business recommendations

**Duties & Responsibilities**
- Identify and develop Predictive and Prescriptive Models to enable better decision making of business.
- Identify, understand and interpret data structures across various databases within the business to facilitate data analysis and continuous monitoring activities.
- Delve for insights in data and processes to help improve the business.
- Contribute to the development of differentiated; superior solutions that meet stakeholder and business requirements through analysis; business requirements gathering and designs validation.
- Ensure product and/or solution design is congruent with the required business specifications through meeting stakeholder requirements timeously.
- Enable the realisation of the financial business benefits accruing including minimisation of operational costs by ensuring that solutions are implemented effectively.
- Model and frame business scenarios that are meaningful and which impact on critical business processes and/or decisions.
- Participate and/or lead discovery processes with business stakeholders to identify problems and opportunities that may be addressed with statistical modelling or machine learning.
- Collaborate with business to define approach to resolution of key business problems or development of new business strategies.
- Contribute to the business by highlighting possible opportunities for process improvement and business value creation.
- Identify and develop the hypothesis testing framework and modelling approach to address the business requirements.
- Identify available and relevant data, potentially leveraging new data collection processes such as social media.
- Make strategic recommendations on data collection and experimental design incorporating business requirements and knowledge of best practices.
- Prepare the data for analysis and modelling, which includes data cleaning, standardisation, transformation, dimension reduction and feature engineering.
- Identify and train suitable models/algorithms to discover patterns and make predictions.
- Extend existing code and develop custom code to implement statistical models, machine learning algorithms and data mining techniques for large datasets in a computationally efficient manner.
- Compare model performance, select the best algorithm for the job and be able to motivate this choice in a non-technical manner.
- Interpret results and translate findings into clear and actionable insights that can be easily validated with the project sponsor.
- Communicate findings to business with various skill levels and in various roles, presenting trends, correlations and patterns found in complicated datasets in a manner that clearly and concisely conveys meaningful insights.
- Assist business users in the use of the models and interpretation of model output.
- Assist with monitoring and reporting on model accuracy after it has been embedded in operations.
- Provide authoritative, expertise and advice to clients and stakeholders.
- Build and maintain relationships with clients and internal and external stakeholders.
- Contribute to the process of negotiating objective and realistic service lev