Data scientist

1.Integrating methods from mathematics, statistics and probability modelling using specialized programming languages, tools and techniques

2. Sourcing and preparing data for analysis
3. Identifying, validating and exploiting internal and external data sets generated from a diverse range of processes
4. Developing forward-looking, predictive, real-time, model-based insights to create value and drive effective decision-making
5. Finding, selecting, acquiring and ingesting data sources,
6. Integrating and cleaning data to make it fit for purpose
7. Developing hypotheses and exploring data using models and analytics sandboxes
8. Refining requirements, validating, training and evolving models over time to discover deeper insights, make predictions, or generate recommendations.
9. Using advanced analytic techniques including — but not limited to — data/text mining, machine learning, pattern matching, forecasting, visualization, semantic analysis, sentiment analysis, network and cluster analysis, multivariate statistics, graph analysis, simulation, complex event processing, neural networks.