Manager : Data Science

2 weeks ago

Pretoria, Gauteng, South Africa Fempower Full time

The role of the Manager: Data Science is to lead and innovate traditional actuarial practices by integrating and harnessing the power of advanced analytics, machine learning, and statistics and emerging technologies to improve product value propositions, drive data-driven decision making, streamline risk rating and pricing operations, enhance decision-making, and contribute to strategic initiatives at the intersection of actuarial science and data, including undertaking critical research to develop the company's intelligence regarding critical and emerging risks.

The Manager: Data Science will manage the Specialist: Advanced Analytics, managing deliverables and timelines of data science work for both non-life and life insurance licenses and report to the Chief Actuarial Officer and collaborate with other departments.

Key Performance Areas
  1. Drive the development of business intelligence solutions that provide actionable insights to various business units, enabling informed decision-making.
  2. Design and build analytical solutions to meet business needs.
  3. Develop and implement innovative machine learning and AI models within an actuarial context.
  4. Craft actuarial analytics strategies that align with business goals and regulatory requirements.
  5. Drive analytical reports to guide business decisions.
  6. Track and report on trends in key risk metrics through the publication of regular dashboards and reports to the Chief Actuarial Officer and other Executive Managers of the business.
  7. Provide thought leadership and guidance to others and communicate to stakeholders regularly.
  8. Lead the forecasting of sales and identify solutions to enhance the value of new business and inforce, increasing new business profitability.
Pricing and Advanced Analytics
  1. Lead the development and deployment of advanced analytics, machine learning, and artificial intelligence solutions to extract insights, improve risk management, and enhance customer engagement.
  2. Lead technical pricing on new and renewal business, including the development, maintenance, review, update, and implementation of new rating/pricing methodologies, models, and systems.
  3. Lead the development of calculation tools and/or quotation tools in collaboration with sales and operations departments, pricing department, and IT.
  4. Lead the modeling and rating of special quotations (new and renewal) – large, unusual, complex, or significantly poor claims experience.
Statistical Modelling and Machine Learning
  1. Lead, develop, and apply statistical models to interpret data and draw conclusions. These models include propensity models, time series forecasting, clustering, classification, regression, optimization, and advanced techniques applied on LBI's insurance data.
  2. Design, implement, and evaluate machine learning algorithms to build predictive models and recommendation systems.
  3. Build pricing models for both the short-term insurance and life insurance businesses.
  4. Continuously assess the performance of machine learning/conventional models and make necessary improvements to enhance accuracy and generalization.
  5. Stay up-to-date with the latest data science tools, programming languages, and advanced analytic technologies.
  6. Leverage appropriate software libraries and frameworks for efficient analysis and model development.
  7. Lead climate change and catastrophe modeling: develop, maintain, and manage LBI's extreme risk models.
  8. Collaborate with the Actuarial Team to develop, review, enhance, and update capital and valuation models.
  9. Operationalize and sustain machine learning models and actuarial software.
Data Visualisation and Reporting
  1. Lead the evaluation and quantification of the impact and value generated by analytics models and data products.
  2. Lead the design, implementation, and management of interactive dashboards and visuals for clear communication of insights to guide business decisions.
  3. Manage the delivery of insights, financial analysis, forecasting, and what-if scenarios based on business requirements.
  4. Ensure compliance with industry regulations and standards while pursuing innovation in actuarial practices.
Departmental and People Management
  1. Performance Management
  2. Capacity Planning
PREFERRED MINIMUM EDUCATION AND EXPERIENCE

Master's degree or higher in Data Science, Actuarial Science, Statistics, Computer Science, Mathematics, or a related field. Actuary (AMASSA / FASSA), Statistician or Data Scientist will be advantageous but not necessary. 7-10 years of Actuarial Science, Data Science, Machine Learning, Analytics, Mathematical Statistics, Computer Science, or Business Intelligence experience within data management and analytics (actuarial, statistics, etc.).

Knowledge of Actuarial Software (e.g. Prophet, Moses, Basys) or Programme / Database (e.g. SQL, PowerBI, QlikView, SAS, R, Python, Tableau, or similar).

Machine Learning or Artificial Intelligence Governance, Advanced Microsoft Office, Predictive Analytics, Pricing Methodologies and/or Machine Learning, Statistical/Actuarial models and Analytical Methods, Database and Querying, Familiarity with data privacy regulations (e.g. GDPR, POPIA, AML), Proficiency in data-related technologies and tools (e.g. data lakes, data warehouses, data visualization, machine learning).

Actuarial Methodologies - Knowledge and understanding of the principles, regulations, and legislation pertaining to acceptable actuarial policies, models, methodologies, and processes. Relevant legislation applicable to insurance companies such as the Insurance Act of and associated Prudential Standards Financial Soundness for Insurers as well as International Financial Reporting Standards ('IFR17').

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