Boehringer Ingelheim & E.ON SE.

Diabetic retinopathy screening needs specialists the world doesn't have enough of. Deep learning can classify retinal images at scale, but in a clinical setting a black-box prediction is unusable: a model nobody can interpret is a model nobody will use.

Built a SHAP-based interpretability framework for clinical decision support models across linear, tree-based and deep architectures, comparing XAI methods against established feature selection approaches for clinical trials and drug development. My master thesis took it further: fine-tuned deep learning models, including ResNet50, detecting diabetic retinopathy stages from retinal fundus images, with local colour enhancement and strategic data balancing driving the performance gains. The core of the work was trust: DeepSHAP explanations validated against clinically annotated lesions, so the model's attention could be checked against what ophthalmologists actually look for.

Sales and financial performance across multiple products and countries, with data from 5+ customer solutions serving millions of users, and business decisions waiting on reporting that was slow to produce.

Worked on sales and financial reporting across the customer solutions: managing data acquisition, streamlining the analytics pipeline, and publishing performance reports that compared actuals against forecasts across products and countries. Designed new KPIs that fed directly into business decisions.