From complex data to clear insight
Experienced in querying, cleaning, validating, analysing and communicating data using a rigorous, evidence-led approach.
Data analyst · aspiring credit risk analyst
I turn data into decisions.
I’m Simamkele Xipu, a data analyst with 2+ years of experience applying quantitative analysis, statistical techniques and machine learning to complex business problems—while specialising in explainable AI for credit risk through my MSc in Data Science.
Professional profile
Technical ability with quantitative depth.
A focused profile for data analytics, credit risk, quantitative analytics and data science opportunities.
Explainable AI for Credit Risk
Researching the balance between predictive performance, transparency, fairness, robustness and stakeholder trust in credit-risk models.
Mathematics, computing and scientific thinking
A multidisciplinary academic foundation that supports structured problem-solving, statistical reasoning and computational modelling.
Experience
Built in analytical environments.
More than two years of experience using data to support assurance, risk analysis, reporting and research.
2024 — Present
Data Analyst
Auditor-General South Africa
- Develop and streamline data-driven audit and risk reports, analytical tests and management-ready visualisations.
- Use SQL to query, join, aggregate and validate large datasets, applying CTEs, window functions and query optimisation.
- Use Python and Excel for data cleaning, reconciliation, exploratory analysis and repeatable analytical workflows.
- Build Power BI reporting outputs that translate complex data into clear findings for stakeholders.
- Support data integration, transformation, quality assessment and modelling across structured information systems.
- Apply critical thinking, statistical reasoning and professional judgement to identify patterns, exceptions and potential risk.
Dec 2022 — Apr 2023
Research Intern
National Institute for Theoretical and Computational Sciences · Nelson Mandela University
- Conducted analytical and computational research into the physics of core-collapse supernovae.
- Developed Python-based numerical simulations using the fourth-order Runge–Kutta method.
- Derived Sedov–Taylor solutions to model blast-wave dynamics and compared analytical results with numerical simulations.
- Evaluated model behaviour and simulation accuracy through structured validation and scientific interpretation.
- Collaborated with researchers and contributed to a published report documenting the methodology, simulations and findings.
Toolkit
Technical depth, business context.
A practical toolkit for extracting, modelling, explaining and communicating evidence.
Data & engineering
SQL · Python · Pandas · NumPy · ETL · data integration · transformation · data quality · Excel
Quantitative analytics
Statistics · hypothesis testing · regression · feature engineering · predictive modelling · EDA
Machine learning & BI
Scikit-learn · classification · cross-validation · ROC-AUC · SHAP · LIME · Power BI · Alteryx
Education
Grounded in science. Growing through research.
My foundation in mathematics, physics and computer science shapes how I approach data: rigorously, curiously and with respect for evidence.
MSc e-Science (Data Science)
University of the Witwatersrand
Research: machine-learning credit-risk models using Explainable AI methods, including SHAP and LIME.
BSc Honours in Physics
University of Johannesburg
Advanced scientific research, quantitative analysis and computational modelling.
BSc — Mathematics, Computer Science & Physics
University of Fort Hare
A multidisciplinary foundation in mathematical reasoning, scientific modelling and computing.
Open to opportunities
Let’s solve a meaningful data problem.
I’m interested in Data Analytics, Credit Risk, Quantitative Analytics and Data Science opportunities in South Africa.