Data Science with less than a year in Financial Modelling & Machine Learning
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M.A. in Financial Economics candidate with strong analytical and quantitative skills, including financial modelling, econometrics, statistics, and machine learning. Experienced in applying advanced techniques to real-world problems through academic projects, such as investment appraisal, profit maximization via linear programming, and credit card fraud detection using Python ML models. Proven leadership abilities as an elected School Board Member and General Secretary, demonstrating a commitment to advocacy and event organization.
University of Hyderabad
M.A. · Financial Economics
August 1, 2024 – June 30, 2026
University of Lucknow
B.A. (Honours) · Economics
August 1, 2020 – June 30, 2023
IISER Tirupati
Research Intern
January 1, 2024 – Present
India
Profit Maximisation & Cost Optimisation via Linear Programming
February 1, 2025 – March 1, 2025
• Formulated and solved a constrained profit-maximisation LP model for a TV manufacturer (B&W vs. Colour) using Excel Solver optimal mix: 100 B&W + 350 Colour units, maximising profit to $3,100 • Built a cost-minimisation diet LP model under nutritional constraints (calcium, protein, Vitamin A), achieving minimum cost of Rs. 2.80 per serving at optimal food mix
Investment Appraisal & Bond Valuation Model
January 1, 2025 – February 1, 2025
• Computed NPV for 2 projects across discount rates 1%-30% (1% increments), plotted NPV profiles, and interpreted crossover rate Project A preferred at r < 10%; Project B at r > 10% • Calculated NPV (8%) and IRR for 5 projects (A-D); applied MIRR at 4% reinvestment rate to correct for reinvestment assumption bias • Valued a Rs.1,000 par bond (8% coupon, 6-yr maturity): computed Macaulay Duration (6.55 yrs), Modified Duration (6.07), and Convexity to estimate price sensitivity to ±2% yield shifts
Credit Card Fraud Detection
January 1, 2024 – January 1, 2025
• Built ML models (Logistic Regression, XGBoost, Decision Tree, Random Forest) to detect fraudulent credit card transactions on a highly imbalanced dataset (0.17% fraud rate) of 284,807 European cardholder transactions • Addressed class imbalance using Undersampling, Oversampling, SMOTE, and AdaSyn techniques; evaluated models using ROC-AUC, Sensitivity, Specificity, and F1-Score instead of accuracy to handle imbalance • Achieved best test ROC-AUC of 0.98 with XGBoost on SMOTE-balanced data; applied optimal threshold selection (0.53) to maximise TPR while minimising FPR; conducted cost-benefit analysis to recommend model deployment strategy for banks
Data Analytics & Visualization
Accenture
June 1, 2026 – Present
Power BI Fundamentals
Unknown
June 1, 2026 – Present
SQL
HackerRank
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects show a strong interest in applying quantitative methods to real-world problems, aligning well with a data science role. The diversity of projects (fraud detection, financial modeling, optimization) indicates a broad intellectual curiosity. Involvement in student organizations and volunteer work suggests a team-oriented and engaged individual. The current enrollment in a Master's program in Financial Economics further strengthens the fit for roles requiring analytical rigor and domain knowledge.
Soft Skills & Operational Fit
The candidate's volunteer experience and positions of responsibility indicate good organizational skills, leadership potential, and a proactive attitude. The research internship suggests an ability to conduct structured analysis and deliver reports. These traits are beneficial for collaborative data science environments.