I am an Applied Artificial Intelligence MSc graduate with a Computer Science background and experience across machine learning, NLP, data analytics, software experimentation, requirements analysis and customer-facing operations.
My path has not been limited to one industry. I have worked on ESG risk analysis, cyclone forecasting, cyber-physical monitoring, text classification, data reconciliation, website experimentation and frontline retail operations. That range has taught me how to adapt technical thinking to different problems and audiences.
Comfortable starting before the data is tidy or the question is precise.
Baselines and trade-offs come before headline metrics.
Validity matters as much as accuracy in risk-sensitive work.
Honest reporting builds trust with technical and non-technical stakeholders.
Clarity is part of the deliverable, not an afterthought.
Useful AI lives in systems and processes, not just notebooks.
Customer-facing operations sit alongside research and engineering — and they have made me a clearer communicator and a more reliable colleague.
Work directly with customers and store teams to solve product, order, collection, return and service issues in a fast-paced retail environment. The role has strengthened practical problem-solving, service recovery, operational awareness and communication with non-technical audiences.
Supported website optimisation and A/B-testing activities by implementing frontend variants with JavaScript, HTML and CSS, checking quality before release and collaborating with technical and product stakeholders.
Gathered stakeholder requirements, mapped workflows, prepared software-requirement documentation and translated business needs into structured technical tasks.
Coordinated webinars, outreach, recruitment support, communication and follow-up activities across remote stakeholders.
Multimodal ESG risk prediction using structured ESG indicators, corporate and news text, transformer representations, model comparison and explainability.
AI-focused thesis research led to a Springer-published chapter on deep-learning-based cyclone track forecasting.
Below the MSc and the Springer publication, additional verified credentials.