M.Sc. Data Science, TU Wien · Vienna

Kasra
Zarinehbaf Asadi
AI Engineer

I have always been fascinated by innovation and how it can improve our lives and drive change in society. This led me first to computer science and later to data science and AI. Today I build AI systems that solve real problems, from the first prototype to a service that runs on its own.

Portrait of Kasra Zarinehbaf Asadi
Kasra Zarinehbaf Asadi · AI Engineer · Vienna, Austria

Background

Education

  1. Oct 2022 — Apr 2026

    M.Sc. Data Science, with distinction

    TU Wien · Vienna

  2. Apr — Dec 2025

    Master's thesis research

    Complexity Science Hub · Vienna

  3. Oct 2017 — Mar 2022

    B.Sc. Computer Science

    TU Munich · Munich

Work

  1. May 2024 — Dec 2025

    Data science research assistant

    WU Wien, Institute for International Business

  2. Nov 2018 — Oct 2020

    Working student, programming

    Atos · Munich

Projects

  1. 01Retrieval · Claude Haiku · Remote job postings

    Job search in plain language

    Describe the job you want in your own words in a chat window. An AI will then suggest matching jobs for your request. Optionally, you can then refine your request with follow-up messages in the chat.

    Each message is turned into filters, such as country, seniority and salary. The jobs that pass are ranked by how close their descriptions are to your request, and the best fits are recommended with an explanation.

    • Python
    • Claude Haiku
    • OpenAI embeddings
    • AWS Lambda
    A search for senior data science jobs in Germany paying at least $90k, answered with three matching jobs and a reason for eachA search for senior data science jobs in Germany paying at least $90k, answered with three matching jobs and a reason for each
  2. 02Time series · Day-ahead auction · 15-minute prices

    Austrian electricity price forecasts

    Every morning, a scheduled job forecasts the next day's Austrian electricity price for each 15-minute interval and publishes it before the auction. Once the auction results are out, each forecast is scored against the actual prices.

    New models are released through an MLOps pipeline. A candidate is first tested on past data that was kept out of its training. It then runs in shadow, forecasting every day beside the official forecast, and is scored the same way. Once it has proven better than the official model over time, it replaces it automatically, and the previous model stays ready for rollback.

    • Python
    • ENTSO-E
    • Docker
    • AWS
    Forecast and actual prices for one delivery day, in 15-minute intervalsForecast and actual prices for one delivery day, in 15-minute intervals