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!