Head of Data & AI (Investment Banking)

Be Different; Gauteng; On-site; ZAR 1,200,000 - 2,100,000; Full time

Job summary

Be Different in Gauteng seeks a senior data science lead to guide analytics across unstructured data, build predictive models and drive business outcomes. The role shapes data strategy, validates methodologies, and mentors junior staff while collaborating with stakeholders on governance and technical initiatives.

Responsibilities include deploying scalable ML models, ensuring data quality, and coordinating productionalising automation.

Qualifications

• Post Graduate Diploma or Master’s Degree in Information Studies or Information Technology.
• 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.
• Experience with SAS, R, SPSS; data visualization with Power BI and Tableau.
• 8-10 years proven development experience in software / software engineering.
• Exposure to data governance and regulatory matters; knowledge of IT data principles.
• Project management experience; building models (credit scoring, propensity models, churn).
• Competent in ML programming in R or Python; supplementary MATLAB, Java.
• Familiar with Hadoop, Spark, Kafka; broader ecosystem.

Responsibilities

• Acts as a subject matter expert from a data science perspective and inputs decisions on data science use.
• Educates the organisation on data science perspectives, testing hypotheses and validation.
• Validates and certifies work of other data scientists and guides junior staff.
• Builds machine learning models and utilizes distributed data processing.
• Codes, tests and maintains scientific models and algorithms; identifies trends.
• Develops a comprehensive operational IA plan and tooling for remediation efforts.
• Coordinates productionalisation of automation software for accuracy and maintenance.
• Guides design of complex techniques to answer business questions and drive outcomes.
• Drives analytics and insights within the business unit via advanced models and algorithms.
• Supports governance, data management infrastructure, and production processes.
• Oversees data collection, integration and pre-processing for modelling.
• Presents results to executive leadership and informs future business plans.

Skills

Data mining

Statistical modeling

Machine learning

Leadership

Data visualization

Data governance

ETL understanding

Project management

Communication

Education

Post Graduate Diploma in Information Studies

Master’s Degree in Information Technology

Master’s Degree in Information Studies

Tools

SAS

R

SPSS

Power BI

Tableau

Hadoop

Spark

Kafka

MATLAB

Java

Job description

In order to be considered the following is required:

• Post Graduate Diploma | Master’s Degree in Information Studies or Information Technology
• 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
• Experience with data visualization tools, such as Power BI, Tableau
• 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
• Competent in Machine Learning programming in R or Python, with supplementary still in Matlab, Java
• Familiar with the Hadoop distributed computational platform, including broader ecosystem of tools such as HDFS / Spark / Kafka

Responsibilities:

• 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 utilizes distributed data processing and analysis methodologies
• 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
• Guides the data management and modelling infrastructure requirements and monitors the implementation thereof through the organization’s infrastructure development processes, adherence to the organizations model production processes, including UAT
• Ensures adherence to governance processes to manage the ongoing enhancement and maintenance of business rules
• Conducts regression testing across all relevant systems as required
• Liaise and collaborate with the entire Enterprise Data Office, providing support to the entire department for its data science needs
• Collaborate with subject matter experts to select the relevant sources of information and translates the business requirements into data mining/science outcomes. Supports an executive leadership by identifying and applying best practices in field of advanced analytics (statistics, operations research) across the organization
• Overseeing activities of the junior team members, ensuring proper execution of their duties and alignment with the organizations vision and objectives
• Provide oversights and expertise to the Data Science team
• Required to draw performance reports and strategic proposals form his gathered knowledge and analyses results for senior executive leadership
• Oversees business integration through integrating model outputs into end-point production systems, where requirements must be understood and adopted relating to data collection, integration and retention requirements incorporating business requirements and knowledge of best practices
• Oversees the gathering of data for use in Data Science models, ensuring that chosen datasets best reflect the organizations goals
• Oversees data pre-processing including data manipulation, transformation, normalization, standardization, visualization and derivation of new variables / features
• Utilizes advanced data analytics and mining techniques to analyse data, assessing data validity and usability, reviews result to ensure accuracy, communicates results and insights to executive leadership
• Presents results and recommendations to executive leadership and influences future business plans based on insights using excellent communication, presentation and visualization capabilities
• Supervises and oversees the mining of data using state-of-the-art value extraction methods
• Enhances data collection procedures to include information that is relevant for building data models

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