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Big Data Analytics , Data Science , Machine Learning Engineering , Spark , Time Series available

Big Data Analytics , Data Science , Machine Learning Engineering , Spark , Time Series

available
Profileimage by Anonymous profile, Big Data Analytics , Data Science , Machine Learning Engineering , Spark , Time Series / Zeitreihen
  • 1015 St. Julians Freelancer in
  • Graduation: P.hD. Mathematics / M.Sc. Mathematics (TU Berlin)
  • Hourly-/Daily rates:
  • Languages: Chinese (Full Professional) | German (Native or Bilingual) | English (Full Professional) | Spanish (Full Professional)
  • Last update: 06.07.2021
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KEYWORDS
Big Data Analytics
Time Series Analysis
Business Intelligence
SQL / NoSQL
Tensorflow
Machine Learning ( Python / Pandas / Scikit )
Data Science
Spark SQL and Analytics
Python & Java
AWS & Google Cloud
PROFILE PICTURE
Profileimage by Anonymous profile, Big Data Analytics , Data Science , Machine Learning Engineering , Spark , Time Series / Zeitreihen
ATTACHMENTS
Dr. Thomas Vanck CV (Juli 2021)

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SKILLS
  • Big Data Analytics & Business Intelligence (Spark, SQL, NoSQL, Excel, various visualisation tools, Jupyter Notebooks)
  • Data Science, Time Series, Machine Learning Engineering (Python, Java, Pandas, Scikit Learn, Tensorflow, PyTorch)
  • Complete planning and implementation of Data Science projects (problem formulation, goal setting, technical communication, implementation, evaluation, monitoring and maintenance)
  • Implementation of complex ETL jobs and data transformations (batch and online processing), real-time datastream processing
  • Other skills (incomplete list): C++, Javascript, Flutter, Git, Docker, Apache Kafka, Apache Hive, Hadoop MapReduce, Bash, Python Flask, REST API, Scrum, Amazon Web Services, Google Cloud, Redis, MySQL, PostgreSQL, MongoDB, Apache Cassandra
PROJECT HISTORY
  • 01/2015 - 06/2016
    • Adrule GmbH
    • < 10 employees
    • Marketing, PR and Design
  • Data Scientist, Data Engineering
  • Conceptual design and implementation of a data warehouse infrastructure in the Google Cloud. Creation of ETL jobs to make unstructured data easy to analyse. Analysis and visualization of historical business data.

    Technologies used: Python Machine Learning Stack (Pandas, Scikit Learn), Google Cloud, Google Big Query, MySQL, Apache Spark.
  • 03/2013 - 10/2014
    • MBR Targeting
    • 10-50 employees
    • Internet and Information Technology
  • Machine Learning Engineer
  • Design, coordination and implementation of a real-time bidding system for online marketing to predict click and conversion probabilities. For this purpose, various specialised real-time machine learning algorithms with different objectives were developed and put into live operation.

    Technologies used: Apache Hadoop, Apache Hive, Apache Spark, Redis, PostgresQL, Python Machine Learning Stack consisting of Pandas, Scikit-Learn and C++ (to accelerate individual Python program parts).
  • 05/2012 - 08/2014
    • Hitfox GmbH
    • 50-250 employees
    • Internet and Information Technology
  • Data Scientist
  • Planning and implementation of a business intelligence reporting pipeline, including predictive functionality for the following days of a week.

    Technologies used: Python Machine Learning Stack (Pandas, Scikit Learn), AWS Redshift, AWS, MySQL.
TIME AND SPATIAL FLEXIBILITY
Location: 100% remote (on-site only irregularly possible under certain circumstances)
OTHER
Angebotene Dienstleistungen
Planung, Koordination und Umsetzung von Big Data Analytics Projekten:
Kommunikation, Zielformulierung, Datenbeschaffung und Aufbereitung (ETL), Sicherstellung einer soliden Datenqualität, zuverlässiger Betrieb der Analyse-Prozesse, Visualisierung und Reporting zur sicheren & schnellen Beurteilung von Analyse-Ergebnissen. Herstellerunabhängige Beratung zur Auswahl von Softwares und Tools mit dem besten Kosten-Nutzen Verhältnis für den Kunden.
Data Science / Machine Learning Engineering
Vollständige Umsetzung des sogenannten Data Science Prozesses. Dieser gliedert sich in folgende Schritte:
  1. Problemidentifizierung (erfordert Einbindung verantwortlicher Stakeholder)
  2. Unmissverständliche Zielformulierung und Planung (erfordert Einbindung verantwortlicher Stakeholder)
  3. Datenbeschaffung, -aufbereitung, -transformation, -bereinigung
  4. Modellierung und/oder ermitteln des passenden Vorhersagealgorithmus, Objektive Performance Evaluation
  5. Implementierung in den Live-Betrieb / Deployment
  6. Monitoring und Maintenance
Machine Learning Engineering, d.h. Entwicklung individualisierter Vorhersage-Algorithmen auf Basis unstrukturierter Daten (z.B. Text, Bild, Zeitriehen, etc.).
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