RR
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Last update: 06.09.2022

Data Scientist

Graduation: PhD in Theoretical Physics
Hourly-/Daily rates: show
Languages: German (Native or Bilingual) | English (Full Professional)

Skills

Key Areas of Expertise
  • machine learning
  • time-series forecasting
  • quantitative finance
  • optimization
  • cloud computing
  • using data to answer complex research questions
  • coordinating freelancer teams in an international environment
Statistics & Machine Learning
  • linear models: OLS, WLS, GLS, Lasso and Ridge Regression
  • nonlinear models: Random Forest (classification/regression), Gradient Boosted Models, Gaussian Process Regression, Feed­-Forward Neural Networks, Kalman Filters
Programming
  • R and Python: daily experience since 2011 and 2016, respectively
  • SQL: exposure to Microsoft SQL Server, SQLite
  • JavaScript: created web dashboards via jQuery and DataTables
  • C/C++: used for Arduino projects; sometimes also via Rcpp
  • LaTeX: employed for research reports and documentation (mostly via knitr)
Libraries
  • R: h2o, (d)plyr, data.table, ggplot2, zoo, lubridate, Rcpp, knitr, roxygen2, testthat
  • Python: h2o, pandas, numpy, scikit­-learn, keras, sqlite3, flask, boto3, requests, unittest
Other
  • AWS (EC2, S3)
  • Git, SVN
  • Docker

Project history

01/2020 - Present
Data Science / Quantitative Research

Selected projects:
  • Created machine learning models for energy load forecasting (data exploration, model prototypes, hyper parameter optimization, documentation)
  • Software maintenance for past projects

01/2019 - 12/2019
Data Science / Quantitative Research

Selected projects:

  • Created cloud-based hyper parameter optimization infrastructure supporting Bayesian, random and grid search in Python
  • Developed transaction cost model for global futures markets

  • Worked on time-series cross validation methods for automated hyper parameter optimization


01/2018 - 12/2018
Data Science / Quantitative Research

Selected projects:

  • Developed Random Forest variable selection method for low signal-to-noise ratio data
  • Implemented different weighting schemes for Random Forest

  • Created prototype models (gradient boosted trees and neural networks) for time-series forecasting


01/2017 - 12/2017
Data Science / Quantitative Research

Selected projects:

  • Follow-up project on cloud migration: developed an API interfacing the Amazon cloud (AWS EC2); the API handles the client’s custom requirements regarding logging, cluster management and per-user cost attribution
  • Developed R and Python backends for machine learning model training

  • Implemented hyper-parameter optimization infrastructure in R

  • Initiated, led and worked on a project for time-series prediction with Random Forests; the solution is used by the client (a trading firm) for their main trading strategy

  • Applied statistical methods to detect structural breaks in time-series data


01/2016 - 12/2016
Data Science / Quantitative Research

Selected projects:

  • Developed Random Forest and Neural Network models that generate return forecasts for various financial markets
  • Research on new predictors for financial markets

  • Proof-of-concept project to move machine learning infrastructure into the AWS cloud


07/2015 - 12/2015
Data Science / Quantitative Research

Selected projects:

  • Research on fundamental-data-based predictors for financial markets
  • Developed allocation algorithm based on dynamic programming

  • Developed cost model for ETFs


Local Availability

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