Data Scientist
Areas of responsibility
- Source, cleanse, refine and verify the integrity of data used for analysis
- Extract and visualise insights by integrating complex datasets and using visualisation software to report, where appropriate
- Apply data mining techniques and statistical analysis theory to ensure reliable, valid and credible insights are leveraged and shared across the group
- Support the strategic objectives of the bank, by proactively conducting data analysis on readily available data sources and presenting results in a clear and meaningful manner
- Identify and use appropriate visualisation tools and software to report information and insights in a relevant and meaningful way
- Present findings and influence decision making based on the value extracted through data intelligence throughout the organisation
- Assist fellow team members and business on critical projects and ad hoc tasks when required
Skills required for the role
- Sc or B. Com in Computer Science, Applied Mathematics, Statistics, Data Science and Analytics or equivalent diploma
- One to two years’ experience in data science, visualisation techniques, BI or machine learning
- Proven ability to analyse complex data sets, draw innovative conclusions and report on it
- Experience in a banking environment would be advantageous
- Two to three years’ experience with Microsoft data technologies
- Ability to write complex SQL statements
- Two to three years’ experience with common data science toolkits, such as R, Weka, NumPy, MatLab, SPSS, SAS, Python, etc.
- Two to three years’ experience in visualisation tools, especially PowerBI
- Ability to use excel advance statistical and data features are required
- Two to three years’ experience or Interest in learning UI Path is vital
- A proven track record in statistical skills, such as distributions, statistical testing, regression, etc.
- Two to three years’ experience analysing and interpreting quantitative and qualitative data, and modelling
- Sound understanding and experience with predictive modelling concepts, machine-learning approaches, clustering and classification techniques, and recommendation and optimisation algorithms
- Understanding of machine learning techniques and algorithms
