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Data Science with less than a year in Python, SQL, Power BI, and Machine Learning for Fintech domain
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Assessing your cultural and operational fit
CSE (AI/ML) graduate with hands-on experience in Python, SQL, and Power BI across end-to-end data analysis and machine learning projects in fintech domains. Business questions first, analysis and modeling built around them. Strong SQL fundamentals including window functions and segmentation logic. 180+ DSA problems solved on LeetCode. 4-star SQL on HackerRank.
Shri Shankaracharya Technical Campus
B.Tech · Computer Science and Engineering (AI & ML)
N/A – June 30, 2026
Loan Default Prediction & Portfolio Risk Monitoring
January 1, 2026 – June 1, 2026
Built an end-to-end credit risk pipeline on domain-driven synthetic data (Indian FOIR/CIBIL norms) - engineered financial ratios (EMI-to-Income, Loan-to-Income), credit score buckets (Poor/Fair/Good), and a Risk Interaction feature combining credit score and loan ratio. Benchmarked Logistic Regression before and after feature engineering recall on defaults jumped from 10% to 73% (+630%), validating that domain-driven features are essential for imbalanced credit classification. Evaluated 3 approaches (Logistic Regression, SMOTE + Random Forest, Voting Classifier) selected Random Forest with class_weight='balanced' via RandomizedSearchCV; final model: Recall 81%, Precision 49%, ROC-AUC 0.83. Identified blind spot in underwriting logic: Fair credit + Medium EMI applicants carry 70% default probability - nearly as risky as Poor credit + Medium EMI (74%) - yet treated as low risk by default; recommended EMI cap rule of 40-45% for this segment with projected 15-20% default reduction. Built Power BI risk dashboard: KPI cards (38Bn exposure, 41% weighted default probability, 6.19Bn expected loss, 21% high-risk applicants), feature importance chart, and risk combination matrix.
PhonePe Transaction Analysis
January 1, 2026 – June 1, 2026
Analyzed 300K transactions across 107K users across 4 services - merged datasets; engineered day type and generation-based age segments (Gen Z / Millennials / Gen X / Boomers). Ran Mann-Whitney U test on weekday vs weekend transaction amounts (p=0.54) - statistically confirmed no significant spend difference, disproving weekend promotion assumptions with quantitative evidence. Built RFM segmentation on 100K+ users using quantile-based Recency, Frequency, and Monetary scoring - tagged 23.5K High Value, 30.8K Mid Value, 45.6K Low Value for targeted retention campaigns. Age × Service heatmap revealed Gen X and Millennials drive 63% of total Loan value (94.9Cr, 93.4Cr); Gen Z Loan volume (53.2Cr) flagged as emerging youth credit opportunity. Power BI dashboard: KPI cards (3.47Bn value, 96% success rate, 300K transactions), monthly trend, MoM% KPI, failure reason heatmap, top-5 users by value with Month and Payment Status slicers.
4-star SQL badge
HackerRank
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects are diverse within the fintech domain, showcasing an interest in real-world applications of data science. The focus on end-to-end solutions from data analysis to dashboarding aligns well with a practical, results-oriented culture. However, the lack of professional experience and academic project types limit the assessment of cultural fit in a corporate environment.
Soft Skills & Operational Fit
The candidate demonstrates a business-oriented approach by framing analyses around business questions and providing actionable insights. The detailed project descriptions suggest good problem-solving skills and an ability to work with complex datasets. However, without direct work experience, it's difficult to assess stress handling, team collaboration, or communication clarity in a professional setting.