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Python Engineer with 1+ years in applied econometrics, machine learning & financial modeling
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Assessing your cultural and operational fit
BA (Honours) Economics graduate with a minor in Mathematics and demonstrated research output in applied econometrics. Hands-on experience building panel regression, ARIMA, and machine-learning models in Python on financial datasets. Strong interest in derivatives markets, volatility, and data-driven strategy research; comfortable working under pressure and learning quickly in live problem-solving settings.
Kirori Mal College, University of Delhi
BA (Hons) Economics, Minor in Mathematics · Economics, Mathematics
August 1, 2022 – June 30, 2026
Modern Vidya Niketan School
Class XII (CBSE)
N/A – Present
Modern Vidya Niketan School
Class X (CBSE)
N/A – Present
Unsupervised Customer Segmentation (RFM + K-Means)
June 1, 2026 – Present
Engineered Recency-Frequency-Monetary scores on 10,000 transactions; applied K-Means clustering (elbow & silhouette) to identify five behavioural cohorts; mapped clusters to high-value and at-risk segments.
Time-Series Forecasting of Sectoral Returns
June 1, 2026 – Present
Built ARIMA and OLS regression models on NIFTY sectoral index returns; engineered lagged features and rolling-window aggregates; conducted stationarity (ADF) and autocorrelation diagnostics. Performed walk-forward out-of-sample backtesting; ranked sectors by risk-adjusted returns to surface investible signals.
A/B Testing - Treatment Effect on Workforce Attrition
June 1, 2026 – Present
Designed a controlled experiment, applied two-sample Z-test for proportions, computed p-values and confidence intervals to validate the statistical significance of the treatment effect.
Credit Risk Default Prediction
June 1, 2026 – Present
Trained Random Forest and Decision Tree classifiers on real-world loan data; performed feature engineering, target encoding, and addressed class imbalance via resampling and class-weighting. Evaluated with confusion matrix, precision/recall, ROC-AUC and feature-importance analysis; visualised key risk drivers in an interactive Power BI dashboard.
Undergraduate Dissertation: Green Finance and Market Behaviour: Panel Evidence from India and Global Economies (2016-2024)
January 1, 2025 – June 1, 2026
Built a two-way fixed-effects panel-regression framework across 6 countries × 9 years (India, USA, Australia, Germany, Brazil, China) to test the impact of green-bond issuance intensity on equity-market resilience, depth, and efficiency. Engineered market-resilience measure as the reciprocal of annualised return volatility; constructed depth (market cap / GDP) and efficiency (turnover ratio) using log transforms; built interaction terms to test the OECD Business Confidence Index as a moderating variable. Identified a statistically significant positive Green-Bond-Resilience relationship (β = 669.67, p = 0.032); pre-specified null result on depth interpreted theoretically; performed endogeneity discussion, country/year fixed effects, and robustness checks. Stack: Python (pandas, NumPy, statsmodels, linearmodels, matplotlib), Excel, git.
Supervised Machine Learning
Stanford / DeepLearning.Al
June 1, 2026 – Present
Power BI
Microsoft
June 1, 2026 – Present
Financial Markets
Yale (Coursera)
June 1, 2026 – Present
Advanced Excel
Udemy
June 1, 2026 – Present
Analytics in Python
IBM
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic background in Economics with a minor in Mathematics, coupled with diverse projects in finance and data science, suggests a strong analytical mindset. The projects demonstrate an interest in applying quantitative methods to complex problems, which aligns well with a data-driven culture. The certifications and participation in events indicate a proactive learning attitude and engagement beyond core academics. The target role of 'Python Engineer' aligns with the candidate's demonstrated technical skills in Python and data manipulation, although the experience is primarily academic.
Soft Skills & Operational Fit
The candidate's resume highlights experience in leading a junior team and onboarding corporate partners, suggesting good communication and teamwork skills. Participation in research conferences and a national business conclave indicates initiative and presentation abilities. The summary mentions being 'comfortable working under pressure and learning quickly,' which are positive indicators for operational fit.