Manager: Actuarial Predictive Analytics and

2 weeks ago

Pretoria, Gauteng, South Africa Affirmative Portfolios Full time

Finance ~ Social Sciences

Pretoria
***:
Manager:
Actuarial Predictive Analytics and Modelling - Pretoria - Permanent - Salary: TEC

R

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 68 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.
  • Proactive 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 teamoriented, 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 nonnumerical 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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