Machine Learning Engineer / Data Scientist / Data Engineer / Data Science Project Manager

9/2017 – 6/2021

As a freelance consultant and expert in Deep Learning, Machine Learning and Data Science, I specialized in fraud recognition, product recommendation systems, image recognition/classification, anomaly detection, time series analysis and NLP. I guided agile projects from conception to production and maintenance & optimization.

I focused on eCommerce solutions that leveraged consumer data, product master data & descriptions, product images and sales transactions.

Product Similarity: The goal of the product similarity solution was to improve downstream system performance by identifying similar or related products for a given product, which could then be used as a benchmark or replacement product. The similarity was determined using various modalities, including visual similarity (via an image autoencoder), consumer behavior (using clickstream data) and product descriptions (by using NLP transformer-based models).

Toolkit: AWS, Bitbucket, Confluence, Jenkins, Jira, Jupyter, PySpark, Python, Scrum, TensorFlow

Dynamic Pricing: The goal of this project was to identify poor-performing products in an early stage, uncover any potential product issues, and determine the right actions (such as an optimal price change) to boost performance. The ultimate goal was to gradually replace the existing solution.

Toolkit: AWS, Bitbucket, Confluence, Jenkins, Jira, Jupyter, Matplotlib, PySpark, Python, Scrum, TensorFlow, XGBoost

Consumer Lifetime Value (CLTV): I was responsible for the conception, implementation, and maintenance of the historical and future monetary value attributed to individual consumers. This included regular extensions and adaptations (e.g. for new markets/brands) and deep dive analyses into the model's most important features.

The models, which were based on consumer behavior data, ran in production and were updated on a weekly basis for all consumers. The results (KPIs) were intensively used in downstream systems and for marketing campaigns.

Toolkit: Bitbucket, Confluence, Exasol, Jenkins, Jira, Jupyter, Matplotlib, Python, SHAP, Scrum, XGBoost

Visual Product Embeddings: I was responsible for the conception and implementation of a variational autoencoder based on product images. The source images were filtered, downscaled, and prepared for a convolutional neural network (VAE) that generated embeddings capable of capturing design elements of a product image. These embeddings were used to find similar products and also fed into downstream models to improve product-based models. The solution ran in production and was updated with new images on a weekly basis.

Toolkit: AWS, Bitbucket, Confluence, Exasol, Jenkins, Jira, Jupyter, Keras, Matplotlib, OpenCV, PySpark, Python, SageMaker, Scrum, TensorFlow, Variational Autoencoder

Purchase Propensity Scores: I was responsible for the conception, implementation, and maintenance of a model for predicting consumer purchase intentions. The solution had been running very stably in production for a few years already and the results had made a significant contribution to marketing channels.

Toolkit: Bitbucket, Confluence, Exasol, Jira, Matplotlib, Python, SHAP, SQL, XGBoost

Amazon Web Services (AWS), Agile Methodologie, Big Data, Computer Vision, Confluence, Data Science, Exasol, Git, Jira, Keras, Maschinelles Lernen, Microstrategy, Natural Language Processing, Python, Pytorch, Scrum, SQL, Tableau, Tensorflow

1/2014 – 8/2017

Machine Learning / Data Science Projects

Date: 2017
Technology: TensorFlow, Keras, Convolutional Neural Networks
Use case: Image detection, classification and metadata extraction for product images
Goal: Enrich metadata for product descriptions from images, finding outliers - helping content management teams to improve data quality

Date: 2017
Technology: TensorFlow, Keras, Convolutional Neural Networks
Use case: Prototype for detecting product variants on an image
Goal: Reduce manual effort, fully automate and scale processes

Date: 2016 Technology: TensorFlow, LSTM
Use case: Audio signal analysis and synthesis using Deep Learning
Goal: Various experiments to deconstruct and construct audio signals

Date: 2016
Technology: TensorFlow, LSTM, Convolutional Neural Networks
Use case: Classification and anomaly detection using Deep Learning
Goal: Labeling transactions, find anomalies, reduce manual work

Date: 2015
Technology: Random Forest
Use case: Product recommendation using Random Forest algorithm
Goal: Product recommendation optimized on long time revenue

Date: 2014
Technology: Regression
Use case: Fraud detection
Goal: Find similarities between new customer registrations to prevent multiple registrations (fraud/misusage)

Date: 2014
Technology: Apache Mahout
Use case: Product recommendation using Item-Based Collaborative Filtering
Goal: Recommend similar Products for known users based on user behavior on a marketplace platform

Date: 2014
Technology: Apache Mahout
Use case: Product recommendation using k-nearest neighbors algorithm
Goal: Show similar Products for unknown users on a marketplace platform

Data Science, Big Data, SQL, Agile Methodologie, Jira, Maschinelles Lernen, Mysql, Oracle Database, Projektleitung / Teamleitung, Projektmanagement, Python, Scrum, Ubuntu