Data Analyst • MSc in Data analytics • Berlin 🇩🇪 Python · SQL · Tableau · Power BI · Machine Learning

• 🎓 Graduated with an MSc in Data Analytics from BSBI, Berlin
• 💡 Passionate about *data analytics, **machine learning, and *business intelligence
• 💼 Actively exploring opportunities in Data Analysis and People Analytics
• 🌍 Languages: English (C1), German (C1 in progress)

Master of Science in Data Analytics
Berlin School of Business & Innovation, Germany (2023–2025)

Data Analytics Course
Ironhack, Berlin (2025)

Bachelor of Technology in Electronics and Communication Engineering
PRIST University, India (2016–2020)

Sales Associate — TK Maxx, Potsdamer Platz, Berlin, Germany (2024–2025)

• Assisted customers with purchases and inquiries in a fast-paced retail environment.
• Managed stock organization and supported POS operations.
• Developed strong communication, teamwork, and customer service skills.

Office Assistant — Perfect Engineers and Contractors, India (2020–2021)

• Supported daily administrative and clerical tasks, including documentation and record keeping.
• Assisted the engineering team with project scheduling, quotations, and client communication.
• Improved office efficiency by organizing project files and managing correspondence.

• Developed a machine learning pipeline to predict trip-level fuel costs (98% accuracy).
• Designed Excel dashboards showing savings & performance trends.
• Tools: Python, Pandas, Scikit-learn, SQL, Excel

• Built predictive model using XGBoost to identify profitable buy/sell signals.
• Analyzed financial market data to visualize trading opportunities.
• Tools: Python, XGBoost, Pandas, Matplotlib

• Predicted video game success using classification models & feature importance analysis.
• Explored platform, genre, and release factors influencing sales success.
• Tools: Python, Scikit-learn, Pandas, Matplotlib

• Comparative analysis between Netflix and Amazon Prime content libraries.
• Built interactive Tableau dashboard visualizing genre trends and regional presence.
• Tools: Python, SQL, Tableau

• Analyzed 1,500+ Spotify tracks to identify key features of hit songs.
• Visualized audio trends and correlations between popularity and track characteristics.
• Tools: Python, Seaborn, SQL, Tableau

Programming: Python (Pandas, NumPy, Scikit-learn, XGBoost, Matplotlib)
Databases: MySQL, SQL Server
Visualization: Tableau, Power BI, Excel
Tools: GitHub, Jupyter, VS Code

• 🔗 LinkedIn
• 💻 GitHub
• ✉ Email: [email protected]
• 🌍 Berlin, Germany

⭐ “Turning data into decisions — one insight at a time.”