Data Scientist

6/2018 – 3/2019

• Test of different models and select the best performing with cross validation: Decision Trees, Gradient Boosted Trees, Support Vector Machines and Neural Networks
• Implement automated method for hyperparameter optimization with bayesian optimization and grid seach techniques
• Developing a memory efficient machine learning pipeline, including automated optimization techniques to allow run by non-technical users

SAP HANA, Python

7/2016 – 3/2019

Goals & Questions:
• Can recommendations be further improved by more advanced machine learning methods?
• Which performance can be achieved with different models, from simple and intepretable to very advanced and rather black-box models?
• Is it possible to improve model performance by adding additional features into more advanced models?
• Are more performant models black-boxes or can explanation/interpretation methods be applied?
• Generate ready to use R packages to apply different business scopes
Role:
• Consulting on method selection and coding embedded in a data science team
• Close interaction with peers and communication with stakeholders and internal IT staff to share knowledge and enable them to use the methods
• Coaching of internal IT colleagues to enable them to adopt the methods in practice and transfer knowledge to peers
Methods/Approach:
• Testing of several machine learning algorithms to generate recommendations: Decition Trees, Gradient Boosted Trees, Convolutional Neural Networks
• Evaluate tested models in cross validation settings and implement into existing algorithmic package
• Developing an automated pipeline from data import from databases over pre-processing and modeling to post-processing with prepared recommendations and if possible explanation layers
• Developing memory efficient functions and implement them in an R package for all pre- and post-processing steps for each implemented method
• Further development of an algorithmic R package based on S4 framework from recommenderlab to implement enseble methods and neural networks
Tools: R, Tensorflow mit keras (R), recommenderlab (R), xgboost (R), rpart (R), Python, SAP HANA, R Markdown

SAP HANA, Python

3/2015 – 6/2016

Role:
• Support as external consulant for conceptual work, methods selection and coding (on-site with remote periods)
• Close collaboration with peers from Data Science, Machine Learning and IT
• Frequent communication of results to peers and internal management consulting colleagues in order to spread the concept in the enterprise

Methods/Approach:
• Clustering methods to group customers on customer features, sales and purchasing patterns
• Selection of appropirate algorithms to discover patterns of frequently purchased products (itemset mining) and to derive rules (association rule mining)
• Post-processing and interpretation of results for stakeholders
• Create interactive platform in Shiny to visually explore and explain purchasing patterns, rules and recommendations

SAP HANA, SAP HANA SQLScript, Python