Tasnim Tabassum

Enthusiastic Data Scientist passionate about impactful work

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About Me

I'm a young, energetic data scientist always eager to learn and take on new challenges. With a strong passion for data and AI, I'm excited to contribute to meaningful projects in innovative teams, leveraging cutting-edge technologies to solve real-world problems.

🤖 Machine Learning

🧠 LLMs (Large Language Models)

🐍 Python

📊 Data Analysis

🛠️ MLOps

🗃 SQL

💻 C++

📈 Project Management

Work Experience

Werkstudent Research for AI in Business Transformation

E.ON, Essen

Mar 2024 – Present

  • AI tool research and development with Maastricht University
  • Integration of findings into business operations

Werkstudent Data Management / MLOps

E.ON, Essen

Apr 2023 – Oct 2023

  • Planned and deployed ML projects in the cloud
  • Built tools to streamline future developments

Werkstudent Data Science

Ecolab, Monheim

Oct 2022 – Mar 2023

  • Developed models and visualized data from sensor systems
  • Supported product testing and research activities

Education

Master of Science in Computational Social Systems

RWTH Aachen University

2020 – Present

BSc in Computer Science & Engineering

Hajee Danesh Science and Technology University

2014 – 2019

Projects

People Analytics Dashboard

Interactive ML-powered dashboard predicting employee attrition with 75% accuracy using Random Forest, Streamlit, and statistical analysis of 1,470 employees.

Live Demo → View Code →

Sentiment Analysis

Movie review classifier using Keras, Gensim, and Word2Vec for accurate sentiment prediction.

View Project →

Python Chatbot

LSTM-based conversational chatbot built with NLTK and Keras for natural language processing.

View Project →

Fake News Detector

Advanced fake news detection system using TF-IDF and PassiveAggressiveClassifier achieving 92.8% accuracy.

View Project →

Publications

Bengali Sign Language Recognition (2020)

Achieved 91.1% accuracy using HOG features for comprehensive sign language recognition system.

Read Paper →

Noise Reduction for Sign Language (2018)

Implemented HOG + KNN pipeline for effective noise reduction in sign language recognition systems.

Read Paper →