About Me
Data and analytics professional with 6+ years of experience across data science, machine learning, data engineering and business intelligence, with current responsibility for leading critical performance-reporting and an…
Data and analytics professional with 6+ years of experience across data science, machine learning, data engineering and business intelligence, with current responsibility for leading critical performance-reporting and analytics capabilities in a regulated asset-management environment. I architect and build end-to-end analytical systems spanning data ingestion, validation, databases, quantitative models, APIs, web applications, cloud deployment and automated reporting. My work includes portfolio attribution, hedge-fund P&L analytics, tactical asset allocation, forecasting and practical LLM integration for first-draft investment commentary. I hold an MSc in Physics, cum laude, with a strong foundation in mathematics, statistics and scientific computing.
Experience
Machine Learning Engineer & Performance Reporting Lead - Data Science & Analytics
<p>Architected, built and own the firm's end-to-end production performance reporting platform, integrating portfolio data validation, return calculations, analytics, attribution, workflow automation and client-ready reporting.</p><p> Provide technical and analytical leadership across performance reporting, translating requirements from investment, risk, operations and leadership teams into scalable data products and production analytical systems.</p><p> Designed a centralised PostgreSQL data platform consolidating daily portfolio returns, holdings, weights and historical investment data across more than one million records, with validation controls to improve reliability, consistency and traceability.</p><p> Built and maintain production ETL, data-ingestion and web-data pipelines supporting reporting, alternative-data research, nowcasting, investment analytics and quantitative signal generation.</p><p> Designed executive performance and risk analytics covering alpha, Sharpe ratios, exposures, drawdowns and other investment measures for the CEO, CIO, portfolio managers and risk teams.</p><p> Built Brinson-based portfolio attribution, security-level attribution and a daily hedge-fund P&L calculation engine, connecting raw portfolio data to performance diagnostics and executive-level interpretation.</p><p> Develop and support applications using Python, SQL, PostgreSQL, FastAPI, React and R Shiny; manage AWS infrastructure across EC2, Lambda, S3 and Elastic Beanstalk, with GitHub Actions, Git and CI/CD for deployment automation.</p>
Machine Learning Engineer
<p>Developed forecasting and machine-learning solutions across business use cases, from problem discovery and data preparation through modelling, evaluation, dashboarding and stakeholder communication.</p><p> Led business-discovery activities and translated operational requirements into analytical solution designs and measurable deliverables.</p>
Data Scientist
<p>Developed statistical and time-series forecasting models for complex business and economic problems, including tax-revenue forecasting using ARIMA and VAR models.</p><p> Designed data-processing pipelines and delivered analytics projects across multiple African markets, translating client requirements into analytical solutions and structured project deliverables.</p>
Data Analyst / MSc Researcher
<p>Conducted large-scale data analysis, statistical modelling, simulation, uncertainty quantification and scientific visualisation within the ATLAS experiment at CERN's Large Hadron Collider.</p><p> Worked in an international scientific environment where data quality, reproducibility and statistical confidence were critical.</p>
PROJECTS
Develop Investment Performance & Reporting Platform
<p>Problem: Performance reporting depended on multiple data sources, calculations and operational steps, creating a need for a centralised, controlled and scalable reporting environment.</p><p>Solution: Designed and built an end-to-end platform using Python and FastAPI for the backend, React for the frontend, PostgreSQL for the data layer and AWS for deployment. The system combines ingestion, data-quality checks, performance calculations, attribution, hedge-fund analytics, workflow automation and client-ready outputs. I also integrated LLMs to generate first-draft investment commentary from structured performance and attribution results, with human review before use.</p><p>Impact: Established a central production capability supporting investment, risk, operations and leadership teams while improving the consistency, traceability and automation of reporting and analytics workflows.</p>