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

Fürth, Deutschland
Weltweit
Fürth, Deutschland
Weltweit

Profilanlagen

20250223 CV Andras Molnar.pdf

Skills

Clean CodeJavaPHPForschungAmazon Web ServicesData AnalysisComputer VisionAtlassian ConfluenceAtlassian JiraBig DataBigTable ImplementationsGoogle BigQueryC++ClickstreamCloud ComputingComputerprogrammierungData MiningElasticsearchGitHubApache HadoopPythonMATLABMachine LearningMicrosoft Sql-ServerMySQLNatural Language ProcessingNumPyOctavePhysical Configuration Audit (PCA)VisualisierungRecommendersystemeTensorFlowAtlassian TrelloAnacondaData SciencePyTorchTransfer LearningApache SparkDeep LearningJupyterKerasGitLabGitpandasMatplotlibPytestPySparkScikit-learnSlackXgboostGnu-BetriebssystemPlotlyAtlassian BitbucketStatic Code AnalysisJenkinsOracledb
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.


Frameworks:
  • Keras, PyTorch, scikit-learn, TensorFlow, XGBoost
  • Conda/Anaconda, Jupyter, Matplotlib, NumPy, openCV, pandas, plotly, Poetry
  • MLflow, SageMaker, Vertex AI
Applications:
  • Anomaly Detection, Audio Analysis and Synthesis, Clickstream Analysis, Computer Vision, Content Understanding, Data Analysis, Data Mining, Data Visualisation, Deep Learning, Dynamic Pricing, Fraud Detection, Image Processing, Image Recognition/Classification, Machine Learning, Natural Language Processing (NLP), Natural Language Understanding, Product Similarities, Recommendation Systems, Speech Recognition
Algorithms:
  • Deep Neural Networks, Convolutional Neural Networks, LSTM, (Variational-)Autoencoder, Transformers
  • Hyperparamer Tuning, Transfer Learning
  • Model/Feature Analysis using SHAP
  • Dimensionality-Reduction (PCA, t-SNE, LDA, Autoencoder, UMAP)
Programming Skills:
  • Python
  • C/C++, Java, MATLAB/GNU Octave, PHP
  • Clean Code, PyTest, Static Code Analysis, Unittest
  • Bamboo, Bitbucket, Jenkins, Git, GitHub, GitLab
  • Software Development and Software Architecture
  • Linux, macOS, Windows
Database skills:
  • Apache Spark, BigQuery, Elasticsearch, Exasol, Graylog, Kibana, MS-SQL, MySQL, Oracle DB
Big Data:
  • Amazon Web Services (AWS), EMR, SageMaker, Apache Spark
  • Google Cloud Platform (GCP), BigTable, BigQuery, Vertex AI
  • Hadoop, PySpark
  • FFmpeg for Video Processing
Virtualisation:
  • Docker
  • Kubernetes
Agile-Tools:
  • Confluence, Jira, Miro, Slack, Teams, Trello

Erfahrung

Berufserfahrungmehr als 10 Jahre
Freelancer seit2017

Sprachen

DeutschMutterspracheEnglischverhandlungssicherUngarischverhandlungssicher

Projekthistorie

Machine Learning Engineer / Data Scientist for Search Engines

OTTO

Konsumgüter und Handel

>10.000 Mitarbeiter

As a machine learning engineer and data scientist in the search team at OTTO, my main task is to use
state of the art machine learning techniques to improve the search experience for our customers.
The Solr search engine, which processes 1.000 queries per second and supports around 20 million
product variants 24/7, is central to OTTO's e-commerce platform. All improvements are extensively
tested and validated through online experiments.

Learning to Select: Improved query precision by filtering out irrelevant results through
comprehensive data-driven solutions on clickstream data. Also identified and removed fraudulent
and bot-generated queries to improve model performance and data integrity.

Hybrid Search: Collaborated with two teams to develop a system that integrates both lexical and
semantic search approaches to provide more relevant search results.

Advanced Spell Check: Designed, implemented, validated and brought to production a leading-edge
spell checking system. This solution not only corrects customer spelling errors but also guides them
towards the most relevant products.

Query Intent Detection: I also led the development of a customer query intent detection approach
to identify non-product and navigation queries, and to recognize brand names and their context
within search queries (Named entity recognition and classification).

Toolkit: AWS, GCP, BigQuery, Clickstream Data, FastText, Huggingface Transformers, MLflow, OpenAI
API, SageMaker, AirFlow, Docker, Jenkins, Terraform, Grafana, Prometheus, Elasticsearch, Kibana,
Confluence, Jira, Miro, Agile/Scrum, FastAPI, Poetry, Python, PyTorch, GitHub, Online
Experiments/Testing, Solr, Pair Programming

Large Language Model (LLM) Integration Consultant for Start-Ups

Divers
As an external consultant, I helped startups to use GPT and other large language models (LLMs). I
provided training, evaluated use cases, assessed limitations such as security, performance, accuracy
and explored options/alternatives to the OpenAI API.

Toolkit: Haystack, Hugging Face models, LangChain, Ollama, OpenAI API, Python

Data Product Owner & Solutions Architect / Machine Learning Consultant

RTL Deutschland

Medien und Verlage

5000-10.000 Mitarbeiter

As a freelance consultant and expert in machine learning applications for content understanding, I
supported the RTL Data team in building the next generation multi-purpose platform "RTL+" in
cooperation with Deezer, using visual (video), audio and text data. An integral part of my role was to
manage and balance the needs and expectations of the various stakeholders involved in the project.

The primary goal of this project is to derive and provide additional metadata from the raw content
that can be used by downstream applications such as search, recommendation, and personalization.
The key challenge is to establish a clean, reliable, scalable, and production-ready state-of-the-art
solution for a large number of building blocks and to create an efficient 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 both 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 and music identification.
 
NLP solutions: language detection (fastText), festivity detection, kids content detection, adult content detection, topic modeling (BERTopic), keyword extraction (KeyBERT) and text summarization.
 
Toolkit: Argo Workflows, Confluence, Docker, Elasticsearch, FFmpeg, GitLab CI/CD, Google BigQuery, Google Cloud Platform (GCP), Google Data Studio, Grafana, Hugging Face models, Jira, Jupyter/JupyterLab, Kafka, Kibana, Kubernetes, MLflow, NumPy, pandas, Poetry, Pub/Sub, Python, PyTorch, Scrum, spaCy, SQL, Streamlit, TensorFlow, Terraform

Machine Learning Engineer / Data Scientist / Deep Learning Expert

adidas

Konsumgüter und Handel

>10.000 Mitarbeiter

As a freelance consultant and expert in Machine Learning, Data Science and Deep Learning, 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: Bitbucket, Confluence, Exasol, Jenkins, Jira, Jupyter, Matplotlib, Python, SHAP, SQL, Scrum, XGBoost


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

Kaggle Challenge

Kaggle Challenge: I participated in the "Histopathology Cancer Detection" Kaggle competition, where 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 finished in 26th place out of 1,149 competitors by using advanced (high-speed) training techniques and heavy image augmentations.

Technologies: Python, Jupyter, PyTorch, plot.ly, GitHub

Machine Learning Engineer / Data Scientist / Deep Learning Expert

n/a
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

Zertifikate

Microsoft Azure Databricks for Data Engineering

Coursera

2023

Machine Learning Engineering for Production (MLOps)

DeepLearning.AI

2022

deeplearning.ai - Natural Language Processing Specialization

Coursera Course Certificates

2020

Machine Learning Engineer Nanodegree at Udacity

Udacity

2018

Deep Learning Nanodegree Foundation at Udacity

Udacity

2017

Neural Networks and Deep Learning by deeplearning.ai on Coursera

Coursera Course Certificates

2017

Neural Networks for Machine Learning by University of Toronto on Coursera

Coursera Course Certificates

2016

Machine Learning: Clustering & Retrieval by University of Washington on Coursera

Coursera Course Certificates

2016

Machine Learning: Classification by University of Washington on Coursera

Coursera Course Certificates

2016

Machine Learning With Big Data (2015) by University of California, San Diego on Coursera

Coursera Course Certificates

2016

Machine Learning by Stanford University on Coursera

Coursera Course Certificates

2016

iSAQB® Certified Professional for Software Architecture

iSAQB

2015

Certified Scrum-Master

Boris Gloger

2008

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