Manager: Actuarial Predictive Analytics and

2 weeks ago

Pretoria, South Africa Affirmative Portfolios Full time

**Finance ~ Social Sciences**
**Pretoria**
***:
**Manager: Actuarial Predictive Analytics and Modelling - Pretoria - Permanent - Salary: TEC**
**R1 028 791.00**

**Minimum Job Requirements**

**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.
- Some experience in operationalizing data science solutions or similar product development.

**Competencies**

Managerial:

- Change management
- Coaching and mentoring
- Conflict management
- Critical and innovative thinking
- Direction setting
- Facilitation and Presentation Skills
- People management
- Policy conceptualization and formulation
- Risk Management
- Programme/project management
- Service Delivery Innovation
- Stakeholder development and relations
- Reporting

Technical:

- Excellent verbal and written communication skills.
- Strong Microsoft Excel, Access and MS Word skills.
- Complex problem identification, solving and decision making.
- Sound actuarial judgment.
- Strong technical background in data analytics and modelling.
- Strong planning and time management of both own and direct team's work
- Strong focus on executing plans and delivering results.
- Pro-active approach and keen to take ownership of work.
- Independent and creative in analysing business results, develop appropriate solutions or alternatives to problems and effectively communicating related issues and results to management.
- Result oriented and ability to work under pressure.
- Work well in team-oriented, collaborative environment.
- Excellent analytical, problem solving & data manipulation skills.
- External force and industry knowledge
- Understanding of Actuarial reserving and valuations
- Understanding of Statistical and financial analysis
- Understanding of Forecasting and projections
- Knowledge and understanding of the Data Science Development Cycle: business understanding, data profiling, feature derivation and selection, data modelling, model evaluation, production, monitoring.
- Outstanding problem solving and analytical skills.
- The ability to build, analyse and interpret numerical and non-numerical data to determine potential statistical inferences to inform business and clinical decisions.
- Ability to clean and unify messy and complex data sets for easy access and analysis.
- Ability to conceptualize and frame a problem, develop hypothesis and identify objective measures to estimate accuracy of machine learning/statistical processes and perform testing and validation with careful experiments.
- Big Data platforms and distributed data processing.

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