08/30/2022 updated
100 % available
Data Scientist
Berlin, Germany
Worldwide
MSc Quantitative Finance/BSc Mathematical ScienceR, Python, LaTeX
ETL, Natural Language Processing (NLP), Computer Vision
TensorFlow, scikit‑learn, dplyr, R Shiny
Docker, Google Cloud Platform (GCP)
MySQL, PostgresSQL
Azure DW, Amazon Redshift
Git, Apache Airflow, Plumber, Flask
ETL, Natural Language Processing (NLP), Computer Vision
TensorFlow, scikit‑learn, dplyr, R Shiny
Docker, Google Cloud Platform (GCP)
MySQL, PostgresSQL
Azure DW, Amazon Redshift
Git, Apache Airflow, Plumber, Flask
Languages
DeutschGoodEnglischNative speakerSpanischGood
Project history
* Developed & maintained Azure Analytics DB. Wrote pipeline to transfer data to Amazon Redshift after company merger
* Wrote & put into production a broken battery detection algorithm. This lead to 11% fewer maintenance visits to our
customers in the field, saving the company €235K in 2020
* Performed statistical analysis using the Weibull distribution to predict total battery failures among customer base over 6
month intervals, allowing the Accounting team to accurately provision costs & the Logistics team to order sufficient
replacement batteries. The average time between customer complaint & problem resolution was reduced by 11.5 days
* Automated the creation & distribution of complex monitoring & financial reports, saving approximately 35 hours/month
in manual work for Finance team members
* Used logistic regression to implement tampering detection for customer systems
* Detected fraud schemes using an anomaly detector to identify suspicious activity across the company
* Built a web application using R Shiny which allowed Customer Finance team to interactively analyse our portfolio
* Became the Data department's team lead & mentored Junior Data Scientists
* Wrote & put into production a broken battery detection algorithm. This lead to 11% fewer maintenance visits to our
customers in the field, saving the company €235K in 2020
* Performed statistical analysis using the Weibull distribution to predict total battery failures among customer base over 6
month intervals, allowing the Accounting team to accurately provision costs & the Logistics team to order sufficient
replacement batteries. The average time between customer complaint & problem resolution was reduced by 11.5 days
* Automated the creation & distribution of complex monitoring & financial reports, saving approximately 35 hours/month
in manual work for Finance team members
* Used logistic regression to implement tampering detection for customer systems
* Detected fraud schemes using an anomaly detector to identify suspicious activity across the company
* Built a web application using R Shiny which allowed Customer Finance team to interactively analyse our portfolio
* Became the Data department's team lead & mentored Junior Data Scientists
* Developed suite of unit tests for software project
* Developed iPad application the Infinite Music Machine which was featured on creativeapplications.net