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Data Science with less than a year in ML, Data Analysis & Python
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Statistics and ML professional with a dual MSc in Statistics (MS University Baroda) and Financial Engineering (WorldQuant University) and end-to-end deployment experience across machine learning and data engineering. I have shipped production-grade systems at national hackathons - a 3,940-line spectral ML pipeline for NHAI and an orbital debris defence system for IIT Delhi - and deployed live fintech products. Currently targeting Data Scientist and ML Analyst roles at analytics-driven and product-focused organisations.
The Maharaja Sayajirao University of Baroda
MSc Statistics · Statistics
August 1, 2025 – June 30, 2027
WorldQuant University
MSc Financial Engineering · Financial Engineering
August 1, 2025 – June 30, 2027
The Maharaja Sayajirao University of Baroda
BSc Statistics · Statistics
August 1, 2022 – June 30, 2025
NisusLabs Research And Consulting Solutions
Market Research Analyst Intern
October 1, 2025 – December 1, 2025
India
International Institute of SDGs and Public Policy Research
Data Field Lead - Research Intern
August 1, 2025 – September 1, 2025
India
Edunet Foundation
AI and Machine Learning Intern
June 1, 2025 – July 1, 2025
India
AstraShield Autonomous Orbital Debris Defence System
June 24, 2026 – Present
Built a J2 + atmospheric drag RK4 orbital propagator and BallTree O(N log N) conjunction assessment engine across 50 satellites and 10,000 debris objects – 40-100x faster than naïve pairwise approaches. Implemented CMA-ES maneuver optimiser (8-dimensional encoding) converging 3-5x faster than fixed-sigma genetic algorithms; Kessler Cascade Monte Carlo (300 trials/cluster, NASA Breakup Model + Poisson chain reaction) computing runaway collision probability per cluster. Shipped a FastAPI REST server with 3 grader endpoints, Pydantic validation, and Docker containerisation – built for IIT Delhi National Space Hackathon automated grader evaluation.
Safar-AI Spectral Reflectivity Intelligence for NHAI Highways
June 24, 2026 – Present
Engineered a 3,940-line production system combining a 31-band hyperspectral simulator, CNN feature extractor, and Gradient Boosting regressor achieving R² = 0.9981 and MAE = 0.0063 on 15 engineered features per highway segment. Built ARIMA + LSTM degradation forecasters with 60-day prediction horizons and 90% confidence intervals; integrated SHAP, PDP, what-if, and counterfactual explainability for field engineer transparency. Quantised model to INT8 for Raspberry Pi 4 patrol vehicles with delta-encoded telemetry (~264 bytes/packet) for low-bandwidth 4G transmission – deployed for NHAI Hackathon 2024 evaluation.
FutureFund Life-Path Financial Simulator
June 24, 2026 – Present
Built a Monte Carlo engine (500-5,000 paths) drawing monthly returns from Normal(μ/12, σ/ν12) with configurable volatility; computes P(goal achieved) with P10-P90 percentile breakdown across investment horizons. Shipped a live Streamlit fintech dashboard (Streamlit Cloud) with goal tracking, lifetime wealth projection, NLP-powered AI assistant, and real-time financial health scorecard.
The Salary Matrix Predicting Pay in the Age of Data
June 24, 2026 – Present
Analysed 90,000+ data science professional records; performed missing value treatment, categorical encoding, and EDA to uncover compensation trends by role, experience, and geography. Benchmarked Linear Regression, Random Forest, and XGBoost on R², MSE, and MAE – XGBoost selected as production model with interactive choropleth map deployment.
Data Analytics Job Simulation
Deloitte Australia
June 1, 2026 – Present
McKinsey Forward Program
McKinsey & Company
June 1, 2026 – Present
Data Visualization with R
IBM Developer Skills Network
June 1, 2026 – Present
Python Programming
Skill India Digital Hub
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's diverse project portfolio, ranging from space defense to financial simulation and public policy, indicates a broad interest and adaptability, which aligns well with dynamic, product-focused organizations. Their participation in national hackathons and leadership in organizing a data science competition suggest a proactive, collaborative, and competitive spirit. The dual master's degrees and continuous learning through certifications demonstrate a strong commitment to professional growth and a curious mindset, which are valuable for cultural fit in an innovative environment.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through complex project implementations and a proactive approach to learning new technologies. Their experience in hackathons and project deployment indicates a results-oriented mindset and ability to work under pressure. The leadership role in organizing a data science competition suggests good organizational and collaboration skills. The ability to translate complex statistical output into policy-facing visualizations indicates strong communication and stakeholder management potential.