Data Scientist

• Range of statistical and advanced computational modelling techniques
• Advanced mathematical models and theories
• Elements of various algorithms
• Features and applicability of various data models
• Features, pros and cons of various statistical approaches, algorithms and tools
• Testing procedures to evaluate statistical models
• Impact of changes to algorithms and models on performance outcomes

• Evaluate prospective analytical tools and platforms for their functional capabilities and ability to meet requirements of the analytic environment
• Develop new algorithms to enable the learning, improvement, adaptation or reproduction of outcomes
• Develop regression models, including linear, multiple and logistic regression models
• Develop mathematical models to isolate trends and optimise data-driven decision making
• Create learning models with a discrete set of environment states, actions and reinforcement signals
• Develop testing procedures to evaluate the data model
• Analyse root causes of any issues highlighted
• Facilitate changes to statistical models, to optimise performance and yield intended outcomes
• Apply complex and advanced statistical analysis and modelling techniques
• Uncover underlying relationships among different variables