Dr. Hendrik Hache

As Head of Data at Outfittery GmbH, I defined and executed the company-wide data and AI strategy, built and led a high-performing team of nine, and drove AI integration into core processes. I launched AutoStyling, a fully automated recommendation engine, and improved tools for 160+ stylists with AI-driven features. I led the migration to AWS and DBT, cutting costs by 50% and boosting efficiency by 80%. I partnered with the C-suite on AI strategy and consistently earned top leadership satisfaction.

As Team Lead Data Science & Data Insights at Outfittery, I led and mentored two data teams, scaled the analytics team, and strengthened the company’s data-driven culture. I aligned team OKRs with business goals, delivered ML solutions, and improved cross-functional decision-making. I drove development of AI products like the “sneak-preview” feature and styling tool enhancements, and created 40 hours of Python and SQL training.

As Senior Data Scientist at Outfittery, I built end-to-end data science solutions, developed scalable pipelines and ML models, and partnered with cross-functional teams to turn business needs into data-driven actions. I led ML-driven personalization initiatives, created an A/B-test evaluation framework, and ensured high-quality data products. I also mentored junior data scientists and founded a data science journal club to promote continuous learning.

As Senior Data Scientist & Software Developer at Alacris, I developed predictive technologies to simulate patient drug responses, advancing personalized medicine and virtual clinical trials. I built tools that translated expert knowledge into scalable mathematical models, supervised the creation of visualizations for large biological networks, and led the design and maintenance of a distributed GCP compute system capable of simulating millions of biochemical models.

As a Research Assistant, I co-led a cancer research project developing methods to identify synergistic effects in cancer cells using large-scale time course and network analysis. I built algorithms revealing pathway interactions across 30K+ features, created a network-based approach for classifying cancer types, and optimized parameters for biochemical models with 20K+ components. My work contributed to three publications in leading journals.

For my PhD in Theoretical Biophysics / Bioinformatics, I analyzed gene regulatory networks using forward modeling and reverse engineering, deepening understanding of gene interactions. I built a tool to generate and simulate networks and created a novel neural network–based reverse engineering method for time-series data, outperforming six existing approaches in reconstruction accuracy.

Theoretical Plasmaphysics