I graduated from the University of Pretoria (UP) with diverse qualifications in Mechanical Engineering (BEng), Applied Mathematics
(BSc. HONS) and Financial Engineering (BSc. HONS). During my tenure as an assistant lecturer at the university,
I took a keen interest in Optimization and Operations Research. As such, I initially registered for a Master’s degree in Operations
Research but ultimately made a choice to join the industry at the time and committed all my energy and focus
towards being a practitioner. In the 9-plus years of being in the workforce, I have taken up data science and data engineering roles
data engineering. As a data scientist, I would consult with heads of various business sectors to provide insights and
optimized solutions to numerous problems faced by the transportation, financial, insurance, and marketing sectors. As a
data engineer, I have been spear-heading as well as being actively involved in various teams responsible for the building
and maintaining of different sophisticated data pipelines and data systems, and the overall governance of our on-premises
and cloud warehouse ecosystem for the entire organization (OUTsurance Holdings Ltd, South Africa).
Research focus is on Machine Learning and Optimization in Digital Marketing
Dissertation: Pricing of various American Options with the Longstaff-Schwartz method
Dissertation: The Hamilton approach in describing the longitudinal vibrations of an isotropic stepped rod under specified conditions
Completed undergraduate degrees in both Mechanical Engineering and Applied Mathematics
MATHEMATICAL MODELLING, OPTIMIZATION, AND QUALITATIVE DATA SPECIALIST
• Created and currently maintaining a vehicle logistics auditing tool for internal and external implementers which provides two main
functionalities: establishing possible client data setup irregularities by employing various data mining techniques and proposing
recommended values of specific statistical categories provided by robust optimization routines. The tool is fully automated and web-accessible.
• Project lead and developer of an optimization tool that provides the best configuration settings for the company’s vehicle routing
software applications that provide the best tailor-made scheduling solutions for different clients.
• Periodically perform regression analysis based on scheduler performances of different EXE versions of various optimization products
sold to clients.
• Provided consulting services to banking institutions such as Standard Bank on establishing the best locations for possible ATM placements through geocoding optimization routines that optimally served their expanding client base across the country.
• Part of the development team that solved complex rail operational issues for Transnet Freight Rail, specifically on their coal line
scheduling distribution sector.
DIGITAL MARKETING DATA SCIENTIST
• In charge of assimilating, structuring and reporting on the digital foot print of all prospective and existing clients that engage with the
company via various online platforms and media devices.
• In charge of attribution modelling through data mining of customer journeys with a key focus on improving customer experience by
gathering insights from various digital marketing funnels.
• In charge of conveying online advertisement performances to the creative team in a bid to drive more customers through various
digital marketing funnels and hence, improve customer acquisition.
• In charge of automating the creation of audiences according to customers’ online behavioural trends (user-centered design) using
supervised and unsupervised machine learning techniques such as regression modelling and clustering respectively. This in turn will
dictate the most suitable types of advertisements and products that should be channeled to each customer class.
• In charge of internal data warehouse keeping and reporting on online performance metrics such as leads, quotes, sales, and policy
cancellations.
BUSINESS INTELLIGENCE SPECIALIST: DATA WAREHOUSING AND ENGINEERING
• Client-centric data modeling for standardization purposes via semantic layering across the entire Actuarial department. Models are
deployed to the cloud via Azure ecosystem to facilitate data processing speeds and perform machine learning routines.
• Employing a star-schema approach to modeling critical business measures such as quotes and sales for the entire Actuarial department.
• D.A.G and App development using various platforms such as Apache Airflow and Visual Studio respectively to create ETL pipelines
that connect different external data sources to internal warehousing in order to facilitate analysis and reporting.
• Monitoring and reporting the state of ETL processes used by the Actuarial and Business Intelligence departments.
