Key Performance Areas
Policy review and implementation
Predictive analytics and modelling
Actuarial Analysis
Reporting
Stakeholder Management
People Management
Qualifications
Bachelor’s Degree/Advanced Diploma in Actuarial Science/Mathematics/Statistics/Data Science related qualification.
Postgraduate in a Financial Management/ an Investment Management/ an Accounting/ a Risk Management related qualification will be an added advantage
At least 6 (six) professional actuarial exam passes/exemptions will be an added advantage.
At least one Data Science of the following certifications:
Data Science Certificate, CCP Data Engineer or SAS Academy for Data Science,
Certified Analytics Professional, Dell EMC Data Science Professional Certification Program,
SAS Advanced Analytics Professional Certification,
IBM Data Science Professional Certificate,
Microsoft Azure Data Scientist Associate Certificate,
AWS Certified Data Analytics,
Certified Analytics Professional, Harvard/EdX Professional Certificate in Data Science, DASCA: Senior Data Scientist,
Coursera: Data Science Specialization.
Experience
Relevant 6-8 years’ experience in an actuarial data science related environment of which 2 years must have been in management/supervisory level/area of expertise.
Experience in SAS (Base, Enterprise Guide, and Enterprise Miner), Python or R
Experience in building and implementation of supervised and unsupervised Machine Learning Algorithms to solve meaningful business problems.
Some experience with Python/Microsoft Machine Learning and tools available within the machine learning ecosystem (i.e. NumPy, pandas, matplotlib, SciPy stack) and working in Jupyter notebooks.
Knowledge and practical experience applying machine learning techniques and working in agile development teams.
Some experience in operationalizing data science solutions or similar product development.
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