I am data scientist with product management background and passion for machine learning and financial data. My background is in behavioral science, statistics and human-computer interaction. I worked in venture capital and completed the Data Incubator bootcamp. I work in R and Python, see GitHub. I am particularly interested in:

  • learning at low false positive rate
  • fraud detection / anomaly detection
  • data visualization using RShiny, plot.ly, ggplot2
  • experimental design and evaluation with robust methods (e.g. mixed effects modelling with lme4)
  • algorithmic trading of indices, crypto currencies and arbitrage using machine learning

investments / board memberships

some projects

  • FTSE index prediction: predicting stock indices and futures for binary option trading during the Data Incubator bootcamp
  • crypto currency prediction: machine learning models to auto-trade crypto currencies and find arbitrage opportunities –
  • crypto arbitrage webapp: logging gigabytes of crypto data every week and calculating up to 40.000 arbitrage opportuntities every 10 seconds
  • lead prospecting: applied machine-learning prototype to predict key account customers (~70% accuracy)
  • subscription payment APIs: APIs for solving complex subscription payment use cases
  • SalesForce.com platform – designed and built CRM for growth start-up and led SalesForce recruiting
  • focus.ly:  prototype of distraction-blocking software designed along psychology research to help students concentrate better and reduce procrastination. Based on research on multi-tasking by Stanford professor Clifford Nass

publications, conferences & workshops