Machine Learning Engineer / Data Scientist / Data Engineer / Data Science Project Manager
Aktualisiert am 01.05.2024
Profil
Freiberufler / Selbstständiger
Remote-Arbeit
Verfügbar ab: 01.09.2024
Verfügbar zu: 100%
davon vor Ort: 100%
Data Science
Machine Learning
Deep Learning
SageMaker
AWS
BigData
Clickstream-Analyse
Dynamic Pricing
CTLV
NLP
Computer Vision
Time Series
Python
TensorFlow
PySpark
XGBoost
SHAP
Keras
pandas
numpy
matplotlib
PyTorch
Künstliche Intelligenz
Deutsch
Verhandlungssicher
Englisch
Verhandlungssicher
Ungarisch
Verhandlungssicher

Einsatzorte

Einsatzorte

Deutschland, Schweiz, Österreich


möglich

Projekte

Projekte

2 Jahre 9 Monate
2021-09 - heute

Content Understanding / Metadata Creation from Video, Audio and Text

Data Product Owner & Solution Architect / Machine Learning Consultant at RTL Deutschland Python Scrum Agile
Data Product Owner & Solution Architect / Machine Learning Consultant at RTL Deutschland

  • Freelance consultant and expert for Machine Learning applications for ?content understanding? on visual (video), audio and textual data supporting the RTL Data Team in order to build the next generation multi-purpose platform (RTL+).
  • The key target of this project is to generate additional metadata from the raw content which can be used afterwards by the downstream applications like search, recommendation and personalization. The main challenge is to establish a reliable, scalable and production ready state of the art solution for a large number of building blocks and create an execution pipeline on top of it.

Video based models: 

  • Aesthetic Ranking
  • Dominant Color Extraction
  • End Credits Detection
  • Face Detection
  • Image Quality Detection
  • Logo Detection
  • Mood Detection
  • Object detection and Recognition
  • Place Prediction
  • Scene and Shot-Boundary Detection
  • Shot Type Detection by using and optimizing pre-trained and self-trained models


Audio based models and solutions: 

  • Speech-to-Text transcriptions using Google?s Speech-to- Text API and Whisper from Open-AI on Podcasts and other audio sources


NLP solutions: 

  • language detection (fastText), festivity detection, kids content detection, adult content detection, topic modeling (BERTopic), keyword extraction (KeyBERT) and text summarization

Google Cloud Platform (GCP) Gitlab CI/CD Google BigQuery SQL Terraform Hugging Face models Google Data Studio MLflow Argo Workflows Elasticsearch FFmpeg JIRA Confluence Scrum Python PyTorch TensorFlow pandas NumPy Poetry Jupyter
Python Scrum Agile
RTL Deutschland
Köln
3 Jahre 11 Monate
2017-09 - 2021-07

Consumer Insights and Data Science

Machine Learning Engineer / Data Scientist / Deep Learning Expert at adidas Python R Scrum ...
Machine Learning Engineer / Data Scientist / Deep Learning Expert at adidas

  • Freelance consultant and expert for Deep Learning / Machine Learning / Data Science applications in following areas: 
    • fraud recognition, product recommendation systems, image recognition / image classification, anomaly detection, time series analysis and NLP
    • Guiding the agile (scrum/Kanban) projects from conception to production and maintenance & optimization
  • Main focus on eCommerce solutions based on consumer data, product master data & descriptions, product images and sales transactions


Product Similarity:

  • in order to increase of the downstream system's performance this solution will help to find similar or related products for a particular product which can be used then as a benchmark or replacement
  • The similarity will be determined by various modalities: 
    • visual similarity (image autoencoder), consumer behavior (clickstream data) and product descriptions (NLP transformers)


Skills:

Python, Jupyter, PySpark, TensorFlow, Jira, Bitbucket


Dynamic Pricing:

  • the main goal for this project is to identify poor performing products in an early stage, uncover possible product issues and determine the right actions e.g. optimal price change to boost performance
  • The overarching goal was to gradually replace the existing solution


Skills:

Python, Jupyter, PySpark, XGBoost, matplotlib, TensorFlow


Consumer Lifetime Value:

  • conception, implementation and maintenance for the historical and future monetary value attributed to an individual consumer. Regular extensions and adaptations for e.g. new markets / brands and deep dive into the model's most important features
  • The models are based on consumer behavior data and are running fully in production and will be updated on a weekly basis for all consumers. The results (KPIs) are intensively used in downstream systems and for marketing campaigns.


Skills:

Python, XGBoost, SHAP, matplotlib, Exasol, Jira, Bitbucket


Visual Product Embeddings:

  • conception and implementation of a variational autoencoder based on product images
  • The source images are being filtered, downscaled and prepared for a convolutional neural network (VAE) where the embeddings will be generated
  • These embeddings are able to capture design elements of a product image which can be used to find similar products but also will be fed into downstream models to improve any productbased model
  • The solution is running in production and will be updated with new images on a weekly basis


Skills:

Python, Keras/TensorFlow, PySpark, SageMaker, OpenCV


Purchase Propensity Scores: 

  • conception, implementation and maintenance for modelling the consumer's purchase intention
  • The solution is running very stable in production for a few years already and the results provide a high contribution to the marketing channels


Skills:

Python, XGBoost, SHAP, matplotlib, Exasol, Jira, Bitbucket

Python R Exasol JIRA Confluence Bitbucket XGBoost TensorFlow Keras Spark PySpark AWS SageMaker XGBoost Exasol
Python R Scrum Exasol XGBoost TensorFlow Keras Spark AWS SHAP
Herzogenaurach
5 Monate
2018-11 - 2019-03

Kaggle Challenge

Data Scientist / Machine Learning Expert Python Pytorch
Data Scientist / Machine Learning Expert

  • participating the "Histopathology Cancer Detection" competition
  • The goal was to identify metastatic cancer in medical images
  • My role was to bring state of the art computer vision techniques to the team and to implement an ensemble of models for the submission
  • We have reached #26 from 1.149 competitors using advanced (high-speed) training techniques and heavy image augmentations

Python Jupyter PyTorch plot.ly GitHub
Python Pytorch
3 Monate
2017-04 - 2017-06

Product Image Classification

Deep Learning / Machine Learning Expert Pyhton TensorFlow Keras
Deep Learning / Machine Learning Expert

  • The goal of the project was to build an MVP for a product image classification system to support annotators' workflows and to identify outliers/broken images within the image pool
  • My role was also to educate the team on the latest deep learning/computer vision possibilities and find additional business cases to implement

Pyhton TensorFlow Keras JIRA Confluence Git
Pyhton TensorFlow Keras
Karlsruhe

Aus- und Weiterbildung

Aus- und Weiterbildung

  • Studied computer science at the Friedrich-Alexander University in Erlangen
  • Electrical engineering (focus on data technology) studies at the Georg-Simon-Ohm University of Applied Sciences in Nuremberg

Professional Training:
  • Microsoft Azure Databricks for Data Engineering (Coursera)
  • Coursera and Udacity certificates in the area of Machine Learning und Big Data
  • deeplearning.ai - Machine Learning Engineering for Production (MLOps)
  • deeplearning.ai - Natural Language Processing Specialization
  • Deep Learning Nanodegree Foundation at Udacity
  • Hadoop Platform and Application Framework by University of California, San Diego on Coursera
  • Introduction to Big Data Analytics (2015) by University of California, San Diego on Coursera
  • Learning How to Learn [University of California, San Diego] on Coursera
  • Machine Learning [Stanford University] on Coursera
  • Machine Learning [University of Washington] on Coursera
  • Machine Learning Foundations: A Case Study Approach by University of Washington on Coursera
  • Machine Learning With Big Data (2015) by University of California, San Diego on Coursera
  • Machine Learning: Classification by University of Washington on Coursera
  • Machine Learning: Clustering & Retrieval by University of Washington on Coursera
  • Machine Learning: Regression by University of Washington on Coursera
  • Neural Networks and Deep Learning by deeplearning.ai on Coursera
  • Neural Networks for Machine Learning by University of Toronto on Coursera
  • iSAQB® Domain-Driven Design (DDD) Workshop
  • iSAQB® Certified Professional for Software Architecture
  • Certified Scrum-Master
  • Sun Certified Java Programmer

Position

Position

Deep Learning / Machine Learning / Data Science Expert


Highly skilled and experienced freelance machine learning engineer/consultant with a deep business understanding specialized in state of the art deep learning, machine learning and data science with a proven track record of delivering high-quality results in a fast-paced and production-ready environment.

I have worked on projects for various clients in different industries, using my expertise to help the organisation improve efficiency, reduce costs, and increase revenue through the use of data-driven solutions.

Kompetenzen

Kompetenzen

Top-Skills

Data Science Machine Learning Deep Learning SageMaker AWS BigData Clickstream-Analyse Dynamic Pricing CTLV NLP Computer Vision Time Series Python TensorFlow PySpark XGBoost SHAP Keras pandas numpy matplotlib PyTorch Künstliche Intelligenz

Produkte / Standards / Erfahrungen / Methoden

AWS
Bitbucket
Confluence
Git
JIRA
Keras
SageMaker
Scrum
TensorFlow
XGBoost
PyTorch
GCP
Special skills and core competencies:
  • Teamplay: 
    • Team development
    • coaching
    • team motivation
    • agile values 
  • Main tasks: 
    • data science and machine learning (AI)
    • data engineering
    • project management
    • software architecture, analysis and implementation of requirements
    • data modelling
    • data migration
    • performance optimization
    • test automation
    • agile software development
    • enabling high performance teams
    • infrastructure evaluation and modernization
  • Professional software-development (20+ years experience)


Machine Learning / Deep Learning:

  • TensorFlow, Keras, PyTorch, XGBoost, Transformers (NLP & vision) , LLMs
  • Python (numpy, pandas, scikit-learn, matplotlib, plot.ly)


Big Data:

  • Amazon AWS
  • EMR
  • SageMaker
  • GCP
  • Hadoop
  • PySpark


Web-Technologies:

  • HTML
  • CSS
  • XML/XSLT
  • JavaScript/AJAX 
  • SOAP
  • REST
  • Micro-Services 
  • Google Analytics
  • Adobe Analytics


Software development tools and techniques: 

  • JIRA and Confluence 
  • Bitbucket
  • Git
  • Jenkins
  • GitLab 
  • Codeception
  • JUnit
  • PHPUnit
  • SoapUI
  • PyTest
  • Unittest 
  • SonarQube
  • Selenium
  • Pylint, Clean Code
  • Code Reviews 
  • Agile: 
    • Scrum
    • Kanban

Betriebssysteme

Microsoft Windows
Ubuntu
macOS
 

Programmiersprachen

Assembler
C
C++
Java
MATLAB
PHP
PySpark
Python
Sehr gute Kenntnisse
R
 

Datenbanken

Data modelling
data migration
ETL processes
Optimization
performance tuning
strong SQL skills
MS SQL
Oracle Database
MySQL
Exasol
Elasticsearch
BigQuery

Berechnung / Simulation / Versuch / Validierung

SHAP

Branchen

Branchen

  • Healthcare
  • E-Commerce
  • Fashion
  • Media & Television

Einsatzorte

Einsatzorte

Deutschland, Schweiz, Österreich


möglich

Projekte

Projekte

2 Jahre 9 Monate
2021-09 - heute

Content Understanding / Metadata Creation from Video, Audio and Text

Data Product Owner & Solution Architect / Machine Learning Consultant at RTL Deutschland Python Scrum Agile
Data Product Owner & Solution Architect / Machine Learning Consultant at RTL Deutschland

  • Freelance consultant and expert for Machine Learning applications for ?content understanding? on visual (video), audio and textual data supporting the RTL Data Team in order to build the next generation multi-purpose platform (RTL+).
  • The key target of this project is to generate additional metadata from the raw content which can be used afterwards by the downstream applications like search, recommendation and personalization. The main challenge is to establish a reliable, scalable and production ready state of the art solution for a large number of building blocks and create an execution pipeline on top of it.

Video based models: 

  • Aesthetic Ranking
  • Dominant Color Extraction
  • End Credits Detection
  • Face Detection
  • Image Quality Detection
  • Logo Detection
  • Mood Detection
  • Object detection and Recognition
  • Place Prediction
  • Scene and Shot-Boundary Detection
  • Shot Type Detection by using and optimizing pre-trained and self-trained models


Audio based models and solutions: 

  • Speech-to-Text transcriptions using Google?s Speech-to- Text API and Whisper from Open-AI on Podcasts and other audio sources


NLP solutions: 

  • language detection (fastText), festivity detection, kids content detection, adult content detection, topic modeling (BERTopic), keyword extraction (KeyBERT) and text summarization

Google Cloud Platform (GCP) Gitlab CI/CD Google BigQuery SQL Terraform Hugging Face models Google Data Studio MLflow Argo Workflows Elasticsearch FFmpeg JIRA Confluence Scrum Python PyTorch TensorFlow pandas NumPy Poetry Jupyter
Python Scrum Agile
RTL Deutschland
Köln
3 Jahre 11 Monate
2017-09 - 2021-07

Consumer Insights and Data Science

Machine Learning Engineer / Data Scientist / Deep Learning Expert at adidas Python R Scrum ...
Machine Learning Engineer / Data Scientist / Deep Learning Expert at adidas

  • Freelance consultant and expert for Deep Learning / Machine Learning / Data Science applications in following areas: 
    • fraud recognition, product recommendation systems, image recognition / image classification, anomaly detection, time series analysis and NLP
    • Guiding the agile (scrum/Kanban) projects from conception to production and maintenance & optimization
  • Main focus on eCommerce solutions based on consumer data, product master data & descriptions, product images and sales transactions


Product Similarity:

  • in order to increase of the downstream system's performance this solution will help to find similar or related products for a particular product which can be used then as a benchmark or replacement
  • The similarity will be determined by various modalities: 
    • visual similarity (image autoencoder), consumer behavior (clickstream data) and product descriptions (NLP transformers)


Skills:

Python, Jupyter, PySpark, TensorFlow, Jira, Bitbucket


Dynamic Pricing:

  • the main goal for this project is to identify poor performing products in an early stage, uncover possible product issues and determine the right actions e.g. optimal price change to boost performance
  • The overarching goal was to gradually replace the existing solution


Skills:

Python, Jupyter, PySpark, XGBoost, matplotlib, TensorFlow


Consumer Lifetime Value:

  • conception, implementation and maintenance for the historical and future monetary value attributed to an individual consumer. Regular extensions and adaptations for e.g. new markets / brands and deep dive into the model's most important features
  • The models are based on consumer behavior data and are running fully in production and will be updated on a weekly basis for all consumers. The results (KPIs) are intensively used in downstream systems and for marketing campaigns.


Skills:

Python, XGBoost, SHAP, matplotlib, Exasol, Jira, Bitbucket


Visual Product Embeddings:

  • conception and implementation of a variational autoencoder based on product images
  • The source images are being filtered, downscaled and prepared for a convolutional neural network (VAE) where the embeddings will be generated
  • These embeddings are able to capture design elements of a product image which can be used to find similar products but also will be fed into downstream models to improve any productbased model
  • The solution is running in production and will be updated with new images on a weekly basis


Skills:

Python, Keras/TensorFlow, PySpark, SageMaker, OpenCV


Purchase Propensity Scores: 

  • conception, implementation and maintenance for modelling the consumer's purchase intention
  • The solution is running very stable in production for a few years already and the results provide a high contribution to the marketing channels


Skills:

Python, XGBoost, SHAP, matplotlib, Exasol, Jira, Bitbucket

Python R Exasol JIRA Confluence Bitbucket XGBoost TensorFlow Keras Spark PySpark AWS SageMaker XGBoost Exasol
Python R Scrum Exasol XGBoost TensorFlow Keras Spark AWS SHAP
Herzogenaurach
5 Monate
2018-11 - 2019-03

Kaggle Challenge

Data Scientist / Machine Learning Expert Python Pytorch
Data Scientist / Machine Learning Expert

  • participating the "Histopathology Cancer Detection" competition
  • The goal was to identify metastatic cancer in medical images
  • My role was to bring state of the art computer vision techniques to the team and to implement an ensemble of models for the submission
  • We have reached #26 from 1.149 competitors using advanced (high-speed) training techniques and heavy image augmentations

Python Jupyter PyTorch plot.ly GitHub
Python Pytorch
3 Monate
2017-04 - 2017-06

Product Image Classification

Deep Learning / Machine Learning Expert Pyhton TensorFlow Keras
Deep Learning / Machine Learning Expert

  • The goal of the project was to build an MVP for a product image classification system to support annotators' workflows and to identify outliers/broken images within the image pool
  • My role was also to educate the team on the latest deep learning/computer vision possibilities and find additional business cases to implement

Pyhton TensorFlow Keras JIRA Confluence Git
Pyhton TensorFlow Keras
Karlsruhe

Aus- und Weiterbildung

Aus- und Weiterbildung

  • Studied computer science at the Friedrich-Alexander University in Erlangen
  • Electrical engineering (focus on data technology) studies at the Georg-Simon-Ohm University of Applied Sciences in Nuremberg

Professional Training:
  • Microsoft Azure Databricks for Data Engineering (Coursera)
  • Coursera and Udacity certificates in the area of Machine Learning und Big Data
  • deeplearning.ai - Machine Learning Engineering for Production (MLOps)
  • deeplearning.ai - Natural Language Processing Specialization
  • Deep Learning Nanodegree Foundation at Udacity
  • Hadoop Platform and Application Framework by University of California, San Diego on Coursera
  • Introduction to Big Data Analytics (2015) by University of California, San Diego on Coursera
  • Learning How to Learn [University of California, San Diego] on Coursera
  • Machine Learning [Stanford University] on Coursera
  • Machine Learning [University of Washington] on Coursera
  • Machine Learning Foundations: A Case Study Approach by University of Washington on Coursera
  • Machine Learning With Big Data (2015) by University of California, San Diego on Coursera
  • Machine Learning: Classification by University of Washington on Coursera
  • Machine Learning: Clustering & Retrieval by University of Washington on Coursera
  • Machine Learning: Regression by University of Washington on Coursera
  • Neural Networks and Deep Learning by deeplearning.ai on Coursera
  • Neural Networks for Machine Learning by University of Toronto on Coursera
  • iSAQB® Domain-Driven Design (DDD) Workshop
  • iSAQB® Certified Professional for Software Architecture
  • Certified Scrum-Master
  • Sun Certified Java Programmer

Position

Position

Deep Learning / Machine Learning / Data Science Expert


Highly skilled and experienced freelance machine learning engineer/consultant with a deep business understanding specialized in state of the art deep learning, machine learning and data science with a proven track record of delivering high-quality results in a fast-paced and production-ready environment.

I have worked on projects for various clients in different industries, using my expertise to help the organisation improve efficiency, reduce costs, and increase revenue through the use of data-driven solutions.

Kompetenzen

Kompetenzen

Top-Skills

Data Science Machine Learning Deep Learning SageMaker AWS BigData Clickstream-Analyse Dynamic Pricing CTLV NLP Computer Vision Time Series Python TensorFlow PySpark XGBoost SHAP Keras pandas numpy matplotlib PyTorch Künstliche Intelligenz

Produkte / Standards / Erfahrungen / Methoden

AWS
Bitbucket
Confluence
Git
JIRA
Keras
SageMaker
Scrum
TensorFlow
XGBoost
PyTorch
GCP
Special skills and core competencies:
  • Teamplay: 
    • Team development
    • coaching
    • team motivation
    • agile values 
  • Main tasks: 
    • data science and machine learning (AI)
    • data engineering
    • project management
    • software architecture, analysis and implementation of requirements
    • data modelling
    • data migration
    • performance optimization
    • test automation
    • agile software development
    • enabling high performance teams
    • infrastructure evaluation and modernization
  • Professional software-development (20+ years experience)


Machine Learning / Deep Learning:

  • TensorFlow, Keras, PyTorch, XGBoost, Transformers (NLP & vision) , LLMs
  • Python (numpy, pandas, scikit-learn, matplotlib, plot.ly)


Big Data:

  • Amazon AWS
  • EMR
  • SageMaker
  • GCP
  • Hadoop
  • PySpark


Web-Technologies:

  • HTML
  • CSS
  • XML/XSLT
  • JavaScript/AJAX 
  • SOAP
  • REST
  • Micro-Services 
  • Google Analytics
  • Adobe Analytics


Software development tools and techniques: 

  • JIRA and Confluence 
  • Bitbucket
  • Git
  • Jenkins
  • GitLab 
  • Codeception
  • JUnit
  • PHPUnit
  • SoapUI
  • PyTest
  • Unittest 
  • SonarQube
  • Selenium
  • Pylint, Clean Code
  • Code Reviews 
  • Agile: 
    • Scrum
    • Kanban

Betriebssysteme

Microsoft Windows
Ubuntu
macOS
 

Programmiersprachen

Assembler
C
C++
Java
MATLAB
PHP
PySpark
Python
Sehr gute Kenntnisse
R
 

Datenbanken

Data modelling
data migration
ETL processes
Optimization
performance tuning
strong SQL skills
MS SQL
Oracle Database
MySQL
Exasol
Elasticsearch
BigQuery

Berechnung / Simulation / Versuch / Validierung

SHAP

Branchen

Branchen

  • Healthcare
  • E-Commerce
  • Fashion
  • Media & Television

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