REFERENCES

NATURAL LANGUAGE PROCESSING (NLP)

Industry: Marketing - company size: <300

Project content:

Building a prescriptive model for advertisement text (with revenue uplift 7%) – conducted feasibility study for user recommendations based on recurrent neural networks.

Technologies & methods:

Python, TensorFlow, RNN, Machine & Deep Learning

Industry: Marketing - company size: <300

Project content:

Text Mining: implementation of a new, scalable item based collaborative filtering algorithm for 150 million events per day.

Increase in click through rate by 10% compared to previous implementation – constructed real time streaming pipeline to transform newspaper text into vector format for about 20 texts per second being transformed, inserted and updated each day, implemented a fully automated model retraining pipeline.

Technologies & methods:

Python, Spark, TensorFlow, Machine & Deep Learning, Big Data

Industry: Banking - company size: <1.200

Project content:

Design and implement a Data Science Stack for Text Mining.

Technologies & methods:

Regression, Classification and Apache Spark, Recurrent Neural Networks, Keras, Hadoop

Industry: Media - company size: <5.500

Project content:

Topic modeling: Use LDA-Algorithm to model the topics of German news
Doc2Vec: Use the topic model to transform each document to a topic vector
Similar documents detection: Use the topic model to transform the corpus to a topic matrix and search similar documents using the corpus-topic matrix
Keyword identification: Build a keyword extractor to identify keywords from German news

Technologies & methods:

Python, Gitlab, Jmeter, SonarQube, Docker, Rancher

Industry: E-Commerce - company size: <1.000

Project content:

Analyze the text of product names, descriptions, etc. to improve search quality, develop novel POS and entity tagger and build a prediction model of the product categories by training a feedforward neural network.

Technologies & methods:

Python, NLTK, Sklearn, machine learning, Neural Networks

Industry: Insurance - company size: <1.200

Project content:

Web-mining & Text-analytics: crawl websites, apply text analytics techniques to extract information.
Natural language processing for German: POS-tagging and stemming based on statistical inference; topic and sentiment analysis of the news.

Technologies & methods:

R, R-Shiny, IBM Watson