Business Segment: Corporate & Investment Banking
Location: ZA, GP, Johannesburg, Baker Street 30
To comprehend and analyze phenomena, apply data mining techniques and statistical analysis to massive, structured and unstructured data sets. Work together with clients, data and technology teams to model difficult business problems, uncover insights and opportunities through statistical, algorithmic, machine learning and visualization tools, and transform data into crucial information that can be used to make informed business decisions. Automate intelligently and use predictive modeling.
Participation in problem definition, data cleaning, hypothesis creation and testing, model training and testing, model deployment, and monitoring of expected results are all necessary for the function. Insights must be successfully communicated to stakeholders, both vocally and visually, together with the pertinent recommendations and the business value that must be made evident.
Qualifications
Minimum Qualifications
Bachelors Graduate in Data Science / Applied Mathematics / Econometrics / Actuarial Sciences/Machine learning / Artificial Intelligence or any other STEM qualification
Experience Required
Data & Analytics
3-7 years
Experience in working with unstructured data (e.g. Streams, images) Understanding of data flows, data architecture, ETL and processing of structured and unstructured data. Using data mining to discover new patterns from large datasets. Implement standard and proprietary algorithms for handling and processing data. Experience with common data science toolkits, such as SAS, R, SPSS, etc. Experience with data visualisation tools, such as Power BI, Tableau, etc.
3-7 years
Proven development experience in software and software engineering. Understanding of financial services data processes, systems, and products. Experience in technical business intelligence. Knowledge of IT infrastructure and data principles. Project management experience. Exposure to governance and regulatory matters as it relates to data. Experience in building models (credit scoring, propensity models, churn, etc.).
Additional Information
Adopting Practical ApproachesArticulating InformationChallenging IdeasExamining InformationExploring PossibilitiesInteracting with PeopleInterpreting DataProducing OutputProviding InsightsTeam WorkingNone Negotiables: 3 years experience, prior model training experience, presentations and stakeholder management.
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