Karim Kacper Alaa El-Din, Python Developer in London, United Kingdom
Karim Kacper Alaa El-Din

Python Developer in London, United Kingdom

Member since August 1, 2022
Karim is a PhD candidate at the University of Oxford with a master's degree in physics from Imperial College London. Aside from his experience in research environments, Karim has worked as the CTO of a green tech app startup, where he combined his expertise in software development, data science, and interdisciplinary work to deliver a fully fleshed-out product. Karim is looking to apply his mathematical, analytical, and software development skills to projects with a real-world impact.
Karim is now available for hire




London, United Kingdom



Preferred Environment

Linux, PyCharm, Poetry, Flutter, Python 3, C++17, Firebase, Google Cloud, PostgreSQL, Python, Jupyter Notebook, Pandas, Pytest, Amazon Web Services (AWS), Tableau, Web Scraping, Data Analysis, Machine Learning, Dart, Node.js, Selenium

The most amazing...

...software I've developed is a social sustainability app with the entire front and back end, pop-up notifications, consumption tracking, and user chats.


  • Senior Python Web Scraping Specialist

    2022 - 2022
    Number Five House Ltd
    • Developed and deployed a web scraping pipeline with Python and Selenium.
    • Collected profile and network data from a large online social media platform.
    • Performed ETL on the data. Used network analysis and machine learning to enrich collected information.
    Technologies: Python, Web Scraping, Google Sheets API, Google Sheets
  • Undergraduate Researcher

    2021 - 2022
    Imperial College London
    • Built a TensorFlow machine learning pipeline using Python to predict the properties of high-energy X-ray pulses at ultrafast rates.
    • Deployed machine learning pipelines to the university's high-performance computing cluster using Secure Shell.
    • Developed simulations of quantum many-body physics in Python and devised a new measurement scheme to analyze simulations.
    • Used advanced statistics and machine learning, including restricted Boltzmann machine neural networks, to extract knowledge from our simulations.
    • Co-authored two papers currently in preparation, both applying machine learning to different physics regimes.
    Technologies: Python 3, HPCC Systems, Machine Learning, Applied Mathematics, TensorFlow, PyTorch, Data Analytics, Data Visualization, Data Science, Artificial Intelligence (AI), Python, APIs, Jupyter Notebook, Pandas, Pytest, Data Reporting, Statistical Modeling, Web Scraping, Data Analysis, Big Data, Google Sheets API, Google Sheets
  • Research Intern

    2020 - 2020
    The Institute of Cancer Research
    • Developed an unsupervised learning pipeline to analyze genetic risk factor pathways for brain tumors in adults.
    • Programmed and debugged R and Python to contribute to interdisciplinary research.
    • Implemented my pipeline on a high-performance computing cluster.
    • Updated the legacy code to use Python 3 instead of Python 2.
    • Performed tissue-specific analysis to find significant risk factors that would not be recognized as significant without accounting for tissue differences.
    Technologies: R, Python 3, Data Science, Genetics, Machine Learning, Data Analytics, Data Visualization, Artificial Intelligence (AI), Python, SQL, Jupyter Notebook, Pandas, Data Engineering, Pytest, Data Reporting, Statistical Modeling, Tableau, Data Mining, Data Analysis, Big Data, STATA, Google Sheets API, Google Sheets


  • Cross-platform Social Sustainability App

    Created a full-stack cross-platform app with a Flutter front-end and TypeScript back-end, connected with a GraphQL API. I built the database on top of PostgreSQL, programmed the back end in TypeScript, and used Firebase for the real-time database, pop-up notifications, and user analytics. Additionally, I enhanced the analytics by using a custom asynchronous data pipeline, which performed k-nearest neighbor analysis to group users into different, dynamically set categories.

  • Webscraping Tutorials in Python

    A set of tutorials for beginner, over intermediate to advanced web scraping in Python using Selenium. The tutorials are written as little challenges, and solutions, as well as explanations, are provided. This project is still ongoing, and I am working on further tutorials to expand toward highly advanced web scraping problems.

  • Bayesian Analysis of Stock Market Data

    Using Excel, I worked on a Bayesian approach that an investment company utilized to predict the likelihood of a recession occurring in the near future, given key market parameters and a decade-long history of market data. As this was just after COVID-19 was brought under control, this was a highly relevant subject, and the employer was more than happy with my work.


  • Languages

    Python 3, Python, Dart, R, C++17, GraphQL, TypeScript, SQL, Markdown, JavaScript
  • Libraries/APIs

    Pandas, TensorFlow, PyTorch, Node.js, NumPy, Google Sheets API
  • Tools

    Google Analytics, Tableau, PyCharm, Firebase Analytics, Pytest, STATA, Google Sheets
  • Paradigms

    Data Science, ETL
  • Platforms

    Jupyter Notebook, Linux, Firebase, Azure, Amazon Web Services (AWS)
  • Other

    Computational Physics, Machine Learning, Data Analytics, Data Visualization, Data Reporting, Statistical Modeling, Web Scraping, Data Analysis, Physics Simulations, Applied Mathematics, Poetry, HPCC Systems, Mobile Analytics, Artificial Intelligence (AI), Data Mining, Big Data, Genetics, ML ops, APIs, Data Engineering, ETL Tools, Business Analysis, Excel 365, Bayesian Statistics, Statistics
  • Frameworks

    Flutter, Selenium, Scrapy
  • Storage

    Google Cloud, Databases, PostgreSQL


  • Ph.D. in Progress in Atomic and Laser Physics
    2022 - 2022
    University of Oxford - Oxford, UK
  • Master's Degree in Physics with Theoretical Physics
    2018 - 2022
    Imperial College London - London, UK


  • Azure Data Science Associate
    MAY 2021 - MAY 2023

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