ELVIS SHIKWAMBANA

Reason for leaving : Focus on my business

• Spearheaded the development of real-time data management solutions for transactional, account, and customer notification events.
• Expertly applied SAS Event Stream Processing (ESP) to architect and deploy high-performance data streaming infrastructures.
• Fostered collaboration with interdisciplinary teams to elicit requirements, convert them into robust data streaming solutions.
• Initiated performance metrics to oversee data streaming operations, pinpointing and addressing inefficiencies.
• Vigilantly maintained customer data integrity and security, proactively complying with stringent data governance protocols.
• Provided technical mentorship, cultivating a culture of excellence in real-time data processing and analytical methodologies.
• Authored detailed documentation, ensuring seamless knowledge transfer and system upgrades.
• Kept abreast of emerging trends in data streaming technology, advocating for strategic upgrades to enhance operational efficiency.

Reason for leaving : New Opportunity

• Managed and optimized Scorecards, Direct Marketing Models, and a Collection of Modelling solutions for recurring model optimization, delivering actionable business insights for growth.
• Developed and refined the bank's application scorecards for collections, risk, and fraud, ensuring robust organizational data, enabling risk differentiation through statistical modeling.
• Spearheaded the creation of the bank's predictive modeling framework, reducing time-to-market for new implementations from 6 months to mere weeks.
• Automated a custom program for seamless conversion of SAS scoring engines, ensuring scalability and reliability, into the Base SAS environment.
• Developed an Automation System in the Base SAS environment to streamline analytics workflows within the credit operations.
• Established an in-house daily next-best action (NBA) system using statistical response models and event trigger analytics, integrated with the credit marketing process to optimize Economic Profit.
• Established strong relationships with clients and stakeholders, ensuring long-term partnerships and repeat business.
• Developed a SAS modelling tool, and the Automated Modelling and Segmentation Tool is a game-changing, flexible solution that will embed the power of predictive modelling into any organizational culture, and it will improves ability to acquire and retain the right customers, expand our customer base and to treat our customers as individuals. This project won the innovation prize of 1 Million in 2017.
• Developed a model deployment solution that can deploy a predictive model in a matter of a few minutes; in the past, it took almost three months to deploy a predictive model on the scoring engine, but the system has significantly reduced the amount of time that it takes to do so. This project was one of the finalists for the innovation prize in 2019, and it incorporates code for a PMML exporter that is already present in Base SAS. When launching models into scoring engine platforms, having this code available is beneficial.

Reason for leaving : New opportunity

• Engineered and sustained a proprietary daily Next-Best-Action (NBA) system, utilizing statistical response models and segment-based analytics, ensuring real-time, customer-specific engagement, employee, and channel KPIs.
• Successfully deployed the NBA system to widespread use among customer service teams, incorporating follow-up strategies and policy compliance.
• Contacted frequency protocols, ensuring system adherence to seamlessly integrated the NBA system into the ring staff, meet, and other channels, to ensure Economic Profit while respecting operational limitations.

Reason for Leaving : New Opportunity

• Designed and implemented enterprise data quality frameworks to improve business intelligence accuracy and support strategic development initiatives.
• Established and executed robust data quality control and monitoring mechanisms to ensure data reliability and governance compliance.
• Defined critical data requirements aligned to business objectives and system integration needs.
• Developed and enforced comprehensive business rules to safeguard data integrity and maintain constraint adherence.
• Maintained and optimised enterprise data models to ensure structural accuracy, performance, and scalability.
• Partnered with ETL teams to define detailed data cleansing and transformation specifications.
• Proactively identified and communicated data quality issues to stakeholders, ensuring timely resolution and minimal operational impact.
• Ensured enterprise-wide compliance with data standards, precision requirements, and governance policies across all data assets.

Reason for leaving : New Opportunity

• Employed SAS to automate South Africa's Retail's legacy CLMS using BASE SAS.
• Skillfully developed automated solutions to optimize the SAS 9 Marketing Automation System.
• Successfully integrated the SAS 9 CLMS into a unified system, aligning with DEV/QA/PROD environments.
• Managed and upheld ABSA Group's Data Governance standards.
• Served as a key technical stakeholder in the implementation of CLO, aimed at consolidating all existing CLMS systems.