Career journey of Thabang Mashinini-Sekgoto

1. BSc in Computational & Applied Mathematics and Astronomy, University of the Witwatersrand (2014 - 2016). Foundations in mathematics, astrophysics & simulation. Mathematical modelling, astrophysics, simulation, and numerical methods.
2. BSc Honours in Computer Science, University of the Witwatersrand (2017). Wildfire estimation with kernel density estimators. Project: Wildfire Estimation Using Kernel Density Estimators.
3. System Analyst & Support, ZAR X (Mar 2017 - Nov 2017). Operational support for a newly launched stock exchange serving 13,000+ clients. Provided systems analysis and operational support for a newly launched stock exchange serving 13,000+ clients, keeping the trading platform stable through go-live and early operations as requirements changed week to week. An early foundation in financial systems, integration, documentation and operational reliability.
4. Data Scientist & Software Engineer, Council for Scientific and Industrial Research (CSIR) (Nov 2017 - Jan 2018). Recognised by Mail & Guardian, CSIR & DST for innovation. Built Django-based predictive analytics and decision-support systems serving 17 municipalities, including the City of Cape Town and 16 across Gauteng, giving public-sector stakeholders operational visibility and real-time access to analytics they could plan against. Applied machine learning and data engineering to surface operational bottlenecks and improve service-delivery planning, working in multidisciplinary teams spanning software engineering, analytics and public-sector innovation.
5. MSc in Computer Science (Distinction), University of the Witwatersrand (2018 - 2019). Distinction · Echo State Networks for image segmentation. Thesis: Learning Level Set Method by Echo State Network for Image Segmentation. Proposed a novel spatiotemporal deep-learning formulation of variational level set segmentation, benchmarking five recurrent and convolutional architectures.
6. Data Scientist, Business Intelligence Services - University of the Witwatersrand (Jun 2018 - Apr 2020). R2M+ annual subsidy impact attributed to the initiative · analytics and ML workshops for staff and students. Built analytics, reporting and machine-learning systems for university-wide institutional planning, student success and decision support using Python, SQL, Power BI and statistical modelling. Developed student recommendation and progression analytics to help faculties and institutional teams see where students needed support and make better academic planning decisions. Built a clustering-based recommendation system for the Faculty of Humanities, linking analytical work to student progression and government subsidy outcomes, with more than R2 million in annual subsidy impact attributed to the initiative. Facilitated analytics and machine-learning workshops for staff and students, helping institutional researchers and analysts adopt practical modelling in their own work.
7. Machine Learning Research Scientist, IBM Research (Apr 2020 - Nov 2021). Models into a petabyte-scale geospatial platform · provincial COVID-19 planning dashboard · NeurIPS 2020 CCAI workshop paper. Conducted applied machine-learning research in climate, environmental intelligence, remote sensing and geospatial analytics, combining scientific experimentation with production-oriented engineering. Worked with large geospatial, satellite and environmental datasets in distributed environments using TensorFlow and related tooling, developing forecasting and predictive models that could operate beyond single-machine research workflows. Deployed climate-forecasting models into IBM PAIRS Geoscope, connecting research outputs to a petabyte-scale geospatial-temporal platform used for environmental and enterprise analytics. Contributed to the Gauteng COVID-19 risk-index and prediction dashboard with IBM Research Africa, Wits University and the GCRO, supporting hotspot identification and healthcare-resource planning for the Gauteng Provincial Department of Health. Co-authored research with international scientists and engineers, contributing across experimentation, model development, evaluation, data pipelines and operationalisation.
8. Senior Data Scientist, Vodacom (Nov 2021 - Mar 2024). ≈R1B annual operational savings · tens of millions of events a day across 15,000+ sites · Vodacom Star Award 2022. Led a team of 10 data scientists and ML engineers building real-time analytics, optimisation and decision-intelligence systems for national telecommunications infrastructure, combining machine learning, mathematical optimisation, streaming data and software engineering on problems including generator optimisation, traffic forecasting, infrastructure planning, anomaly detection, resource allocation and site prioritisation. Led the Smart Generator Optimisation work across more than 15,000 sites, contributing approximately R1 billion in annual operational savings through better allocation and use of mobile power infrastructure. Built and designed streaming systems processing tens of millions of daily telemetry and alarm events using Kafka, PyFlink, PySpark and Kubernetes. Established reusable engineering practices and technical standards across the team, and mentored practitioners while working closely with network, engineering, operations and business stakeholders. Received the Vodacom Star Award in 2022 for engineering contribution and impact.
9. Lead Data Scientist, Absa Group (Mar 2024 - Present). Ubunye Engine at 100M+ events a day for 10+ products · telematics from months to under a day. Took data science from infrastructure not built for big data to a modern platform: led the modernisation strategy and architected the data engineering capability from scratch, bringing Databricks into the bank with CI/CD and integration with legacy systems, as part of the move from on premises to cloud (AWS and Databricks). That capability runs on Ubunye Engine, which processes 100M+ events a day and is the engineering layer for 10+ products. Modernised telematics, about 7 to 10 million trip records a day, from months to under a day, and the flood forecasting used by underwriting. Leading the hyper personalisation strategy with customer lifetime value and segmentation models. Lead a team of three data scientists and introduced AI and model governance aligned to the bank’s standards.
10. PhD in Computer Science, University of the Witwatersrand (Commencing 2027). Proposed research: physics-informed self-supervised learning for SAR flood mapping. Commencing in 2027; the proposal is in preparation and not yet registered. The proposed research explores physics-informed self-supervised learning for SAR-based flood extent mapping, with applications to data-scarce climate and insurance-risk settings. It follows on from the MSc work on echo state networks for level set segmentation, and from the geospatial and climate risk systems built at IBM Research and in insurance.