freiberufler Full Stack Data Scientist | Data Driven Strategist | Analytics & Reporting auf

Full Stack Data Scientist | Data Driven Strategist | Analytics & Reporting

zuletzt online vor wenigen Stunden
  • auf Anfrage
  • 1030 Wien
  • Weltweit
  • en  |  de
  • 13.01.2023


I have more than 10 years of experience in machine learning and data science. During my employments, I have lead teams of several data scientists, both near- and off-shore and have built use cases delivering several hundred thousand € profit.

Ich biete

  • Amazon Web Services (AWS)
  • Apache Hadoop
  • Big Data
  • Business Intelligence (BI)
  • Cloud Computing
  • Data Science
  • Google Cloud
  • Machinelles Lernen (allg.)
  • Natural Language Processing (NLP)
  • Predictive analytics

Projekt‐ & Berufserfahrung

Research Data Scientist
Max Planck Institute of Plasma Physics, Greifswald
6/2022 – 12/2022 (7 Monate)
Hochschulen und Forschungseinrichtungen

6/2022 – 12/2022


Automated Scientific Discovery – Use of ML methods to ease the cycle of scientific discovery. Implemented an automated ML platform with several explainable AI and causality frameworks.
Dask | Ray | Pytorch | Python | Optuna | LIME| SHAP | Anchor | ASV | SHAPLEY FLOW | Captum | FastAPI

Eingesetzte Qualifikationen

Cloud (allg.), Python

Full Stack Data Scientist
IU International University of Applied Sciences, München
3/2022 – 6/2022 (4 Monate)
Hochschulen und Forschungseinrichtungen

3/2022 – 6/2022


Development of a feature-store to help boost model development process. Automation of machine learning models using the complete MLOps stack of AWS including CI/CD, model pipelining, deployment, and scheduling.
ETL scripts to load data google analytics, flatten the key/value structure of the data, and finally save it in AWS S3. Built a model for text labeling to better understand student feedbacks.

Eingesetzte Qualifikationen

Apache Spark, Business Intelligence (BI), Datenanalyse, ETL, Keras, Natural Language Processing (NLP), PostgreSQL, Python, PyTorch, TensorFlow, Textklassifikation

Lead Developer
FetchCFD UG, Erlangen
3/2021 – 4/2021 (2 Monate)

3/2021 – 4/2021


Implemented a Twitter bot which search through tweets on topics of interest, summarizes the text on those tweets, perform sentiment analysis and then publish the story on the timeline. Found @empirisch2. Tools used were Python| Tweepy |NLTK | Hugging Face T5 | Sentiment Analysis

Eingesetzte Qualifikationen

Keras, Natural Language Processing (NLP), Python, TensorFlow

Lead Developer
FetchCFD UG, Erlangen
2/2021 – 2/2021 (1 Monat)
Search Engine

2/2021 – 2/2021


Implemented a search engine for DIY projects. The platform is live at findingdiydotcom. Tools used were Python| Django | Selenium | Hugging Face T5 | NLTK | Sentiment Analysis.

Eingesetzte Qualifikationen

Django (Framework), Natural Language Processing (NLP), Selenium

Lead Developer
FetchCFD UG, Erlangen
1/2021 – 1/2021 (1 Monat)
Machine Learning

1/2021 – 1/2021


Implemented a search engine for 3D printable models. The platform is live at 3dfindabledotcom. Tools used are Python | Django| Selenium | VGG16.

Eingesetzte Qualifikationen

Django (Framework), Natural Language Processing (NLP), Selenium

Full Stack Data Scientist
Kundenname anonymisiert, Wien
3/2019 – 8/2019 (6 Monate)
Data Science

3/2019 – 8/2019


Price forecasting of blue-chip stocks.
Python | SQL | LSTM | RNN | Boosting | SVM | GNU | Scikit-Learn | Keras | FBProphet | Optuna | State-Space Modeling | ARIMA | Kalman Filter | ta-lib

Eingesetzte Qualifikationen

Apache Hadoop, Gradient Boosting, Keras, Natural Language Processing (NLP), Python, Rekurrentes Neuronales Netzwerk (RNN), Scikit-learn, Support Vector Machine (SVM)

Lead Full Stack Data Scientist (Festanstellung)
T-Mobile GmbH, Vienna, Austria, Wien
4/2017 – 12/2021 (4 Jahre, 9 Monate)
Data Science

4/2017 – 12/2021


MODELS FOR CROSS-SELLING (02.2021 - 11.2021)
Several machine learning models are built to help facilitate the cross-selling activities of a telecom operator. The models are built to, 1) identify the likely customer for cross-selling, 2) the most suitable product for that customer and lastly, 3) the right channel to approach that customer. Campaign success rate increased by 17%.
Python | SQL | DWH | CI/CD | Oozi | Tableau | Google Analytics | Big Query | Boosting | Bagging | Optuna

MODELS FOR UP-SELLING (03.2021 - 08.2021)
Built machine learning models to help facilitate the up-selling activities. This is to facilitate the base-management challenges, i.e., increase ARPU per customer by up-scaling their tariffs. Campaign success rate increased by 28%.
Python | SQL | DWH | CI/CD | Oozi | Tableau | Google Analytics | Big Query | Scikit-Learn | Optuna

LIFT AND SHIFT 08.2020 - 01.2021
The end-to-end execution of household identification use-case was too time consuming and was block other pipelines. Therefore, a lift and shift approach was adopted to fully migrate the use-case to the GCP, including its ETL.
Pyspark | Cloud Storage | GCP Dataproc | GCP Data Studio | GCP Cloud Composer | AirFlow

Built a model to identify customers living in same household for a telecom operator. Extensive explorative data analysis is performed to identify potential levers to help improve the matching criteria.
Pyspark | Hive | SQL | CI/CD | Oozi | Tableau | Google Analytics | Big Query | MLIB | Graph Theory

PROACTIVE MAINTENANCE (07.2019 - 03.2020)
Proactively detect the root causes of network problems. A thorough time-series analysis is performed and both induction (ML) as well as deduction-based models are built. Time to resolve the ticket was reduced by half.
Pyspark | Hive| SQL | Power BI | CI/CD | Oozi | Tableau | ElasticSearch

TARIFF OPTIMIZATION (02.2019 - 05.2019)
Sketch a relation between the old products and current customer base and identify any gaps in the current portfolio which hinders the operators in increasing their customer base. Additionally, analyze the need for any shadow products for retention and customer base management.
R | R FOR OPERATIONS RESEARCH | Google ortools | Crone | R&D| Clustering

VALUE BASED NETWORK ROLL-OUT (05.2018 - 02.2019)
Based on several customer satisfaction KPIs, a model is built to efficiently target network rollout and upgrade activities by investing in sites with poor network quality scores. This results in saving of several million euros as network rollout is a high budget activity i.e., a small % improvement in investment strategy results in considerable savings.
Pyspark | Hive | Power BI | Traffic Forecasting | xgboost | Oozi | R&D | Analytical Models

CUSTOMER EXPERIENCE (09.2017 - 05.2018)
Several models are built to measure customer experience on various service aspects. E.g., customer satisfaction via NPS, bill shock, customer experience w.r.t network quality, as well as service line interactions. Several inductions (ML) and deduction-based models are built.
Pyspark | Hive | Python | SQL | Power BI |Oozi | Analytical Models

CHURN & RETENTION (05.2017 - 09.2017)
Built a model to predict customer churn. Additionally, a detailed analysis on churn reasons is performed by calculating the SHAPLEY values. After A/B testing, an ultimate 17% reduction in churn was achieved.
Python | SQL| Power BI | DNN | Boosting | SVM | Bayesian Statistics | SHAPLEY | Crone Job | Scikit-Learn | Keras

TRAFFIC LOAD FORECASTING (04.2018 - 07.2020)
Forecasting network traffic per cell basis. This project was the pre-requisite of value-based roll-out.
Python | SQL| LSTM| RNN | Boosting | SVM | GNU | Scikit-Learn | Keras | FBProphet | Optuna | State-Space Modeling | ARIMA | Kalman Filter | DeepAR | AWS SageMaker

Eingesetzte Qualifikationen

Apache Hadoop, Apache Spark, Big Data, elasticSearch, Google Analytics, Machinelles Lernen (allg.), Microsoft Power BI, Python, R (Programmiersprache), Scikit-learn, Tableau

Application Developer (Festanstellung)
Samsung, Graz
2/2016 – 2/2016 (1 Monat)
IT & Entwicklung

2/2016 – 2/2016


Worked as an application to develop battery softwares

Eingesetzte Qualifikationen

C, C++, Python


Modernizing Data Lakes and Data Warehouses with GCP (Coursera)
Januar 2022
Building Batch Data Pipelines on GCP (Coursera)
Januar 2022
Building Resilient Streaming Analytics Systems on GCP (Coursera)
Januar 2022
Google Cloud Big Data and Machine Learning Fundamentals (Coursera)
Dezember 2021
Building a Data Science Team (Coursera)
April 2018
Structuring Machine Learning Projects (Coursera)
April 2018
Neural Networks and Deep Learning (Coursera)
April 2018
Improving Deep Neural Networks (Coursera)
April 2018
Convolutional Neural Networks (Coursera)
April 2018
Fundamentals of Machine Learning in Finance (Coursera)
April 2018
Guided Tour of Machine Learning in Finance (Coursera)
April 2018
Finance for Non-Finance Professionals (Coursera)
April 2018



Jahr: 2017
Ort: Alpen-Adria-Universität Klagenfurt, Österreich

Jahr: 2011
Ort: RWTH Aachen, Germany

Jahr: 2007
Ort: UET Lahore, Pakistan


Pyspark | Scrum | SQL | Git | Python | Oozi | Airflow | Google Analytics | BigQuery | GCP Dataflow | Data Fusion | GCP Dataproc | GCP Datalab | Data Studio | CI/CD | AWS SageMaker | Power BI | Tableau | Qlik | Django | Selenium | PostgreSQL | DWH | Hive | Elastic | Microservices | | Scikit-Learn | MLIB | LSTM | RNN | GNU | NLTK | Hugging Face | OR-Tools | Ensemble modeling | Pytorch | Keras | Tensor Flow | Scikit-Optimize | FBProphet | DeepAR

Persönliche Daten

  • Englisch (Muttersprache)
  • Deutsch (Gut)
  • Europäische Union
  • Schweiz
  • Vereinigte Staaten von Amerika
10 Jahre und 9 Monate (seit 04/2012)
2 Jahre


Nur registrierte PREMIUM-Mitglieder von können Kontaktdaten einsehen.

Jetzt Mitglied werden