Sebastian Napiorkowski available

Sebastian Napiorkowski

Big Data Solutions Architect & Machine Learning Engineer

available
Profileimage by Sebastian Napiorkowski Big Data Solutions Architect & Machine Learning Engineer from London
  • SW2 5JS London Freelancer in
  • Graduation: M. Sc. Computer Science, RWTH Aachen University
  • Hourly-/Daily rates:
  • Languages: German (Native or Bilingual) | English (Full Professional)
  • Last update: 08.12.2020
KEYWORDS
PROFILE PICTURE
Profileimage by Sebastian Napiorkowski Big Data Solutions Architect & Machine Learning Engineer from London
SKILLS
Backend: Python, Django, Java, Jersey, Jetty, Java Script ES6, Node.js, Express, Android, Cloudera/Hortonworks Stack, Apache NiFi, Apache Kafka, Apache Spark
Databases: MySQL, PostgreSQL Apache HBase, Apache Hadoop, MangoDB, CouchDB, ElasticSearch, ELK-Stack, Cassandra, Memcached, Redis
Frontend: HTML 5, React, Java Script ES6, jQuery, d3.js, Android, Kibana, Tableau, Amazon Quicksight
Deployment: Docker, Amazon AWS ecosystem, EC2, S3, Google App Engine, Heroku, Apache, Linux Server
Data Science: numpy, sci-kit-learn, sci-py, pandas, machine learning, feature engineering, natural language processing, clustering
Miscellaneous: Git, Svn, Jira, Confluence, Slack, Google Analytics, IoT
PROJECT HISTORY
  • 07/2019 - 09/2020

    • DAX30 group (Ludwigshafen am Rhein)
    • >10.000 employees
    • Automotive and vehicle construction
  • Big Data Solution Architect & Information Retrieval Specialist
  • Conceptualization, architecture and development of a scalable Big-Data solution (as a R&D Datalake use case) for mass indexing file contents using bleeding edge natural language processing and machine learning algorithms on the Cloudera Hadoop Stack (HDP and HDF), Palantir Foundry and Kubernetes Deployment in Microsoft Azure.

    Technologies used
    • NiFi and MiNiFi for ETL
    • Apache Spark Processing (Java, Scala & Python)
    • Apache HBase
    • Elasticsearch Stack
    • Django Backend
    • React Frontend
    Features comprising i.a.
    • Raw text extraction from various file types
    • Language dependent indexing
    • Clustering Approaches (i.a. Latent Dirichlet Allocation, Latent Semantic Indexing, doc2vec)
    • Parse unstructured data into structured data
    • Named Entity Recognition (i.a. chemical entities) using Neural Networks
    • Entity Linking (Distant Knowledge)
    • Molecular Substructure Search

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