Hello, I'm 👩💻 Daria Vasylieva 👋
🚀 Career Transition Journey 🚀
Manual QA Engineer (10 years) → Machine Learning & Data Analysis Enthusiast
🎯 Current Focus: Machine Learning & Data Analysis Leveraging 10 years of quality assurance expertise to build a new career in data science and machine learning. My analytical mindset and attention to detail from QA perfectly complement the precision required in ML and data analysis.
💼 My Unique Value Proposition QA Mindset + Data Science = Robust, Reliable ML Solutions
- Quality First: 10 years of ensuring software quality translates to meticulous data validation
- Bug Hunter: Skilled at identifying anomalies and edge cases in datasets
- Process Oriented: Strong methodology in testing approaches data preprocessing
- Detail Focused: Critical thinking skills essential for feature engineering
- 🤖 Machine Learning: Supervised & Unsupervised Learning, Model Evaluation
- 📊 Data Analysis: Statistical Analysis, Data Visualization, EDA
- 🐍 Python: pandas, numpy, scikit-learn, matplotlib, seaborn
- 🧠 Deep Learning: Neural Networks, TensorFlow, Keras
- 📈 Data Tools: Jupyter Notebooks, SQL, Excel Advanced Analytics
🛠️ Tech Stack 🎓 Currently Learning
- Python
- Pandas
- NumPy
- Scikit Learn
- TensorFlow
- Jupyter
🔧 QA Experience (10 Years)
- Selenium
- SQL
- and more ;)
📈 GitHub Stats
After a decade in manual QA, I've developed an analytical mindset and attention to detail that perfectly aligns with data science. My experience in:
- Data Validation: Ensuring data integrity and quality
- Edge Case Identification: Finding outliers and anomalies
- Process Documentation: Creating clear, reproducible workflows
- Cross-functional Collaboration: Working with diverse technical teams
These skills are directly transferable to machine learning and data analysis, where data quality and model reliability are paramount.
💬 Open to opportunities in Data Science, Machine Learning, and Data Analysis roles!
