About Benjamin Greve

Hi, my name is Ben Greve. I’m a mathematician and data scientist from Germany. I build data products to automatize complex data-driven tasks – thereby making value-creating processes fast, reliable and scalable.

I believe that data can allow you to see where you are in relation to where you want to go. It can tell you what is working and what isn’t, help you find hidden potential in your business or let you spot inefficient processes and fraud.

Professional Experience

I have 8+ years of experience working with data.

REWE Systems GmbH (Cologne)

2017 ○○●●●●●●●●●●
2018 ●●●●●●●●●●●●
2019 ●●●●●●●●●●●●
2020 ●●●●●●●●●●●●
2021 ●●●●●●●●●●●●
2022 ●●●○○○○○○○○○
(and ongoing)

Automatically providing tens of millions of customers with a fully personalized selection from hundreds of offers – every day. Optimizing offer selection based on past purchasing behavior in order to maximize revenue, redemption rates and customer satisfaction. Building robust and efficient data pipelines to calculate hundreds of statistics for billions of customer-offer combinations. Developing, training, evaluating and deploying dozens of predictive models. Reducing complexity and interdependencies.

meinestadt.de GmbH (Cologne)

2015 ○○○○○○●●●●●●
2016 ●●●●●●●●●●●●
2017 ●●○○○○○○○○○○

Developing and implementing a predictive model to identify poorly performing job ads in the website’s job market section and automatically launch targeted counter measures. Implementing a web-based software framework for product managers to manage content exports to affiliates. Performing data integration of tracking data and webserver logs on a CDH big data cluster and running analyses on the cluster to answer business-side questions.

PricewaterhouseCoopers GmbH (Stuttgart)

2013 ○○○○●●●●●●●●
2014 ●●●●●●●●●●●●
2015 ●●●●●●○○○○○○

Data analysis, data mining, statistical programming and visualization. Developing and implementing algorithms to detect fraudulent behavioural patterns in online shop orders and activities. Developing and pitching use cases of Big Data technologies for clients across all industries.

Forensic data analysis. Detecting anomalies in SAP accounting data using ACL, Microsoft SQL Server and QlikView (various large national and international enterprises). Text mining in large amounts of semi-structured text documents using Java and Lucene (financial services). Automatic recognition of repeating patterns in categorical time series data to identify fraudulent behaviour (retail industry).

Leibniz-Institut für Präventionsforschung und Epidemiologie (Bremen)

2011 ○○○○○○○○○●●●
2012 ●●●●●●●●●●●○
2013 ○○●●○○○○○○○○

Publication of results of my diploma thesis about clustering methods for dietary pattern analysis.

Administration of EU projects (6th and 7th Framework Programm for Research and Technological Development, European Commission) and communication with the project partners.

Technical Experience

SQL●●●●●Git, Bitbucket●●●●○
R & RStudio●●●●●Hive●●●●○
KNIME●●●●●Unix shell script●●●●○
Excel●●●●●Java●●●○○
Python●●●●○

Other technologies: Impala, SAS, C++, MATLAB, XSLT, Logi Analytics, QlikView, Tableau, LaTeX, Gephi, Neo4j, Microsoft PowerPoint, Microstrategy, Control-M, Zeppelin, Jupyter

Concepts: Scrum, OOP, Gitflow, ETL, data quality, webtracking, reporting, Android app development, graph databases

Publications

Social Media Links