AI Engineer with 1+ years in Machine Learning & Financial Analytics
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Economics graduate from IIT Kanpur (Minor:ML & Management Sciences) with strong foundations in financial analytics, credit risk modeling, and business operations. Proficient in Python, SQL, Power BI, quantitative modeling, and statistical inference with hands-on experience in collections analytics, loan default prediction across real-world finance workflows.
Indian Institute of Technology Kanpur
BS · Economic Sciences
August 1, 2022 – June 30, 2026
PySpark Spark SQL XGBoost
Customer Acquisition & Campaign Optimization Platform
June 1, 2026 – July 31, 2026
India
Python LightGBM SHAP Pandas
Loan Default Prediction System
March 1, 2026 – April 30, 2026
India
SQL Window Functions CTES Python
Food Delivery Analytics
January 1, 2026 – February 28, 2026
India
Department of Industries, Government of NCT Delhi
AI Research Intern
June 1, 2025 – July 31, 2025
New Delhi, Delhi, India
Maritime Research Center | Think Tank
ML & Economics Research Intern
June 1, 2025 – August 31, 2025
India
Amex Decision Science
Personalized Offer Ranking
June 1, 2025 – July 31, 2025
India
Customer Acquisition & Campaign Optimization Platform
June 1, 2026 – July 31, 2026
Processed 45,000+ customer records using PySpark & Spark SQL, automating campaign analytics and reducing effort by 70%. Built XGBoost propensity models identifying the top 20% high-conversion prospects, improving targeting precision by 30%. Developed an A/B testing framework using hypothesis testing and confidence intervals, quantifying a 12% conversion uplift. Designed a Power BI dashboard tracking ROI, conversion, and acquisition KPIs, reducing reporting turnaround time by 80%.
Loan Default Prediction System
March 1, 2026 – April 30, 2026
Built credit risk pipeline estimating Probability of Default(PD) and LGD via LightGBM on 50,000+ commercial lending records. Engineered 15+ features including EMI overdue ratio and charge-off indicators, segmenting top 20% high-default-risk borrowers. Deployed SHAP explainability for borrower risk scoring, reducing manual loan servicing review by 40%.
View ProjectFood Delivery Analytics
January 1, 2026 – February 28, 2026
Built retention pipeline on 7-table schema using cohort analysis and RFM segmentation via NTILE() across 15,000+ records. Engineered churn detection and activation funnel using LAG() & anti-join, identifying at-risk segments across monthly cohorts. Quantified campaign ROI via conditional aggregation, surfacing 2× revenue gap between organic and paid channels. Delivered KPI dashboard covering MoM growth, CLV, and SLA breach rates using CTEs and window functions.
Personalized Offer Ranking
June 1, 2025 – July 31, 2025
Processed 30M+ clickstream and transaction records via Parquet, engineering scalable user/session-level ranking features. Trained LambdaMART with SMOTE-balanced data, optimizing ranking via Optuna-tuned Bayesian hyperparameter search. Validated with 5-fold cross-validation, achieving significant MAP@7 lift over keyword baselines across diverse user cohorts.
View ProjectCultural Fit Analysis
The candidate's profile demonstrates a strong cultural fit for an AI Engineer role, particularly in organizations that value data-driven decision-making and practical application of machine learning. The projects span diverse domains like finance (loan default, offer ranking), marketing (customer acquisition), and policy research (semantic search, climate risk), indicating a broad interest and ability to adapt to different business contexts. The use of a wide array of technologies (Python, SQL, PySpark, LightGBM, XGBoost, SHAP, Optuna, FAISS) shows a willingness to learn and apply various tools, which is crucial in a rapidly evolving field like AI. The academic background in Economic Sciences with a minor in ML & Management Sciences from a top-tier institution (IIT Kanpur) provides a solid theoretical foundation combined with a business-oriented perspective. The candidate's involvement in case competitions further highlights problem-solving skills and a drive for impact. The overall profile suggests a candidate who is intellectually curious, technically capable, and eager to apply AI to real-world challenges.
Soft Skills & Operational Fit
The candidate's project descriptions highlight a strong results-oriented approach, with clear quantification of impact (e.g., 'reducing query resolution time by 60%', 'improving targeting precision by 30%'). This indicates a focus on delivering tangible business value. The diversity of projects suggests adaptability and a proactive learning attitude. The experience in internships, particularly in a government department and a think tank, shows an ability to work in structured environments and contribute to policy-relevant research. The detailed descriptions of methodologies (e.g., SMOTE-balanced data, Optuna-tuned Bayesian hyperparameter search, hypothesis testing) suggest a methodical and rigorous approach to problem-solving. While direct evidence of teamwork or stress handling is not explicitly provided, the scope and complexity of the projects imply a capacity for managing challenging tasks.