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Job Summary
Data ScientistPurpose
- To build, optimise and implement innovative quantitative analytical methodologies, procedures, and advanced mathematical models that provide analytical support and interpret insights, to address business opportunities and problems and implement business strategy, under guidance against predicted results and deliver according to set processes and procedures
- Implement localised Analytics strategy to address business needs, under supervision and within set processes and procedures
- Contribute to innovation by finding faster and more accurate ways of working
- Comply with relevant statutory, legislative, policy and governance requirements and adhere to processes and procedures related to area of specialisation
- Build and maintain stakeholder relationships
- Address customer needs to meet or exceed customer expectations
- Act responsibly with work related resources to contribute to cost containment
- Assess own performance through seeking timely and clear feedback and request training where appropriate
- Demonstrate teamwork as a valued team player
- Participate in utilisation, refinement and enhancement of statistical models and data analysis to inform decision making and address business needs
- Contribute to creative business solutions, optimisation of processes and conduct statistical modelling and data analysis to inform strategic decisions, under supervision and within set processes and procedures
- Participate in the delivery of value-add outputs across the analytics value chain in delivery of business strategy
- In accordance with National Credit Act (NCA) candidates applying for this role will require a credit record check.
- Relevant Degree in Maths, Stats, Engineering, Computer Science, Econometrics, Physics or Actuarial Science
- 1 to 3 years’ experience in data environment
- Credit, Pricing, Marketing, CVM, Trading etc.
- Design thinking
- Analytics Ops, Agile and SAFe concepts will assist
- Hands on experience using model such as: Naïve Bayes, Support Vector Machines, Classifications, Boosting Algorithms, Time Series, Feature Engineering and
- Dimensionality Reduction
- Data and Information Management topics e.g. structure, dimensions, storage
- Database management
- Python, SQL, MATLAB, SAS, S-PLUS or R (used for statistical analysis)
- Monte Carlo techniques
- Machine learning
- Data mining and data modelling
- C#/Java, .NET or VBA, Excel
- Calculus (including differential, integral and stochastic)
- Linear algebra and differential equations
- Probability and statistics
- Game theory
- Portfolio theory
- Equity and interest rate derivatives, including exotics
- Systematic and discretionary trading practices
- Credit-risk products
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