Head, Data & Artificial Intelligence, Business Transformation (Corporate & Investment Banking, Technology)

Location: Johannesburg, GP, za

Company Description

Standard Bank Group is a leading Africa-focused financial services group, and an innovative player on the global stage, that offers a variety of career-enhancing opportunities – plus the chance to work alongside some of the sector’s most talented, motivated professionals. Our clients range from individuals, to businesses of all sizes, high net worth families and large multinational corporates and institutions. We’re passionate about creating growth in Africa. Bringing true, meaningful value to our clients and the communities we serve and creating a real sense of purpose for you.

Job Description

Oversee data mining techniques and conduct statistical analysis to large, structured and unstructured data sets to understand and analyse phenomena. Model complex business problems, discovering insights and opportunities through statistical, algorithmic, machine learning and visualisation techniques, working closely with clients, data and technology teams to turn data into critical information used to make sound business decisions. Oversee predictive modelling.

• Acts as a subject matter expert from a data science perspective and provides input into all decisions relating to data science and the use thereof. Educate the organisation on data science perspectives on new approaches, such as testing hypotheses and statistical validation of results. Validates and certifies the work of other data scientists and trains team members in statistical models and guides junior colleagues or less experienced staff on projects and drives leading practice.
• Builds machine learning models from and utilises distributed data processing and analysis methodologies. Competent in Machine Learning programming in R or Python, with supplementary still in Matlab, Java, etc. Familiar with the Hadoop distributed computational platform, including broader ecosystem of tools such as HDFS / Spark / Kafka.
• Codes, tests and maintains scientific models and algorithms; identifies trends, patterns, and discrepancies in data; and determines additional data needed to support insight. Processes, cleanses, and verifies the integrity of data used for analysis.
• Develops, implements, monitors and maintains a comprehensive operational IA plan, rules, methodologies and coding initiatives in order to drive IA for remediation efforts. Develops and co-ordinates a comprehensive strategy for productionalising automation software so that it is accurate and well maintained.
• Guides and validates the design of various complex mathematical, statistical, and simulation techniques to answer critical business questions and create predictive solutions which drive improvement in business outcomes. Drives analytics and insights within required business unit by developing advanced statistical models and computational algorithms based on business initiatives.

Qualifications

• Post Graduate Degree: Information Technology / Information Studies (required)

Additional Information

Experience Required: Data Monetisation

8-10 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.

8-10 years: Proven development experience in software / software engineering.  Up to date with developments in IA field.  Experience in technical business intelligence; in depth understanding of the banks data processes, systems and products. Knowledge of IT infrastructure and data principles forming the basis for data quality management. Project management experience. Exposure to data governance and regulatory matters. Experience in building models (credit scoring, propensity models, churn, etc

Behavioural Competencies:

• Adopting Practical Approaches
• Articulating Information
• Challenging Ideas
• Exploring Possibilities
• Interpreting Data

Technical Competencies:

• Data Analysis
• Database Administration
• Data Integrity
• Knowledge Classification
• Research & Information Gathering