MSc Big Data Science (Distinction) | Data Scientist | Machine Learning Engineer

I'm a versatile data professional with expertise spanning machine learning, financial analytics, and AI development. I transform complex data into actionable insights across multiple domains—from quantitative finance to healthcare predictive modeling.

• 📊 Primary Focus: Data Science | Machine Learning | Statistical Analysis
• 💼 Currently: Data & Admin Coordinator at QMUL School of Business and Management
• 🎓 Education: MSc Big Data Science (Distinction), QMUL | BSc IT (9.6/10 CGPA)
• 💡 Finance Enthusiast: Exploring quantitative methods and risk analytics
• 🔬 Research Background: Published research in AI-driven health monitoring systems

• MSc Big Data Science (Distinction) - Queen Mary University of London
• BSc Information Technology (9.6/10 CGPA) - Top of class performance
• Big Data Science Course Representative - Student leadership & advocacy

• Aavishkar State Research Convention - Presented AI-driven predictive health monitoring
• Dissertation: AI-powered cardiovascular risk assessment achieving 88% accuracy
• Published Research: Predictive analytics for chronic disease management

• Managing accreditation data systems (AACSB, PRME, Athena Swan) for 4,000+ students
• Developed automated workflows reducing manual processes by 60%
• Led cross-functional stakeholder communications for institutional compliance

Financial Risk Analytics | Python | TensorFlow

Discovered that defensive stock diversification collapses by 89.5% during Bitcoin crash events, revealing systemic market dynamics.

• Analyzed 2,199 trading days across crypto and equity markets
• Built LSTM model for stress regime prediction
• Applied regime-switching frameworks and correlation analysis

Impact: Provides portfolio managers with early warning signals for liquidity crises.

Machine Learning | Healthcare Analytics | Predictive Modeling

Developed AI system for cardiovascular risk prediction achieving 88% accuracy using patient vitals and medical history.

• Processed multi-modal health data (vitals, lab results, patient history)
• Implemented ensemble learning (Random Forest, XGBoost, Neural Networks)
• Designed real-time monitoring dashboard for clinical decision support

Impact: Early intervention potential for at-risk patients.

Data Science: Statistical Analysis | Hypothesis Testing | A/B Testing | Experimental Design
Machine Learning: Supervised/Unsupervised Learning | Deep Learning | Model Optimization
Programming: Python | R | SQL | JavaScript
ML Frameworks: TensorFlow | Keras | Scikit-learn | PyTorch | XGBoost

Financial Analytics: Time-series forecasting | Risk modeling | Portfolio optimization
NLP & Text Analytics: Sentiment analysis | Topic modeling | Text classification
Computer Vision: Image classification | Object detection
Big Data: Hadoop | Spark | Distributed computing

Data Processing: Pandas | NumPy | SciPy
Visualization: Matplotlib | Seaborn | Plotly | Tableau | Power BI
Development: Jupyter | Git | Docker | AWS
Databases: MySQL | PostgreSQL | MongoDB

MSc Big Data Science (Distinction) - Queen Mary University of London (2024-2025)

• Specialized in machine learning, statistical modeling, and data engineering
• Dissertation: AI-powered predictive health monitoring systems

BSc Information Technology (9.6/10 CGPA) - Top of Class

• Comprehensive foundation in algorithms, databases, and software engineering

Key Coursework:

• Advanced Machine Learning & Deep Learning
• Statistical Methods for Data Science
• Big Data Processing & Analytics
• Financial Data Analysis
• Cloud Computing & Distributed Systems

• Advocated for 50+ students on curriculum and resource needs
• Organized peer learning sessions and study groups
• Bridged communication between students and faculty

• Aavishkar State Research Convention - Presented AI health monitoring research
• Showcased innovative applications of machine learning in healthcare

• Actively exploring quantitative finance and algorithmic trading
• Contributing to open-source data science projects
• Participating in Kaggle competitions and hackathons

• 🔍 Exploring quantitative finance and algorithmic trading strategies
• 🤖 Building end-to-end ML pipelines with MLOps best practices
• 📊 Developing interactive dashboards for business intelligence
• 🧠 Learning advanced deep learning architectures (Transformers, GANs)
• 💼 Seeking opportunities in Data Science, ML Engineering, and Analytics roles

I'm actively seeking roles in:

✅ Data Scientist - Transforming data into business insights
✅ Machine Learning Engineer - Building production ML systems
✅ Data Analyst - Driving data-driven decision making
✅ Quantitative Analyst - Applying ML to financial markets
✅ Research Scientist - Advancing AI/ML research

Available: Immediately | Location: London, UK (Work Authorization: Graduate Visa)

• 💼 LinkedIn: linkedin.com/in/manasi-nandrajog
• 📧 Email: manasi.nandrajog@gmail.com
• 🐙 GitHub: You're already here! Explore my repositories below ⬇️
• 📊 Portfolio: your-portfolio-website

💡 "Data is the new oil, but insights are the refined fuel that drives innovation."

⭐ If you find my work interesting, feel free to star my repositories and reach out for collaborations!