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Data Scientist with 1+ years in Systemic Risk Modeling & ML Engineering
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Quantitative Data Scientist & Machine Learning Engineer specializing in systemic risk modeling, financial network analytics, and end-to-end predictive ML pipeline development. Skilled in Python, SQL, PySpark, time-series forecasting, fraud detection, NLP, and scalable AI deployment, delivering research-driven models and data-backed decision intelligence for financial applications.
SIES College of Arts, Science and Commerce
MSc · Data Science
August 1, 2024 – June 30, 2026
SIES College of Arts, Science and Commerce
BSc · Information Technology
August 1, 2021 – June 30, 2024
Innomatics Research Labs
GenAI Intern
November 1, 2025 – February 1, 2026
India
Outlier.ai
AI Model Trainer
June 1, 2025 – November 1, 2025
India
MGrid Technologies
Software Developer Intern
November 1, 2024 – June 1, 2025
India
Graph-Based Financial Systemic Risk Prediction Engine
June 1, 2026 – Present
Developed a GNN-based systemic risk engine to simulate market contagion and dual-shock propagation across financial networks (~88-92% prediction reliability via Monte-Carlo validation). Implemented a bounded unsupervised training framework with graph-smoothness and variance constraints to learn non-linear company risk sensitivities. Built an interactive Streamlit dashboard for real-time shock analysis, sector spillover insights, and defensive vs pro-cyclical asset classification.
View ProjectAI-Driven Legal Clause Risk Prediction System
June 1, 2026 – Present
Built a hybrid Legal-AI analysis system combining fine-tuned Legal-BERT (~96-97% estimated classification accuracy) with rule-based NLP to detect contractual risks, classify clauses, and generate structured compliance intelligence. Engineered an end-to-end document intelligence pipeline (OCR → clause extraction → GNN dependency modeling → risk scoring) with FastAPI + Next.js deployment, enabling multilingual contract analysis and interactive risk visualization.
View ProjectDynamic Financial Network Systemic Risk Engine
June 1, 2026 – Present
Built a dynamic correlation-network systemic risk pipeline, modeling shock-regime spillovers across equities with statistically robust graph construction (≥50-observation overlap, |p| ≥ 0.3 thresholding, high-modularity community validation). Engineered a signed asymmetry risk factor to quantify firm-level shock amplification vs stabilization, enabling regime backtested identification of systemic risk drivers and hedge-sensitive portfolio signals.
View ProjectSure Vision AI Multi-Agent Decision Intelligence System
June 1, 2026 – Present
Built a multi-agent LLM-based decision system simulating executive perspectives (CFO, COO, Compliance, HR) to evaluate strategic decisions using structured reasoning and consensus-driven scoring. Developed a scenario simulation and financial impact engine generating best/likely/worst-case outcomes, enabling risk-aware decision-making with quantified savings/loss projections and execution planning.
View ProjectSQL
IBM
June 1, 2026 – Present
AWS
AWS
June 1, 2026 – Present
Data Science
Microsoft learning
June 1, 2026 – Present
Azure
Mircosoft Learning
June 1, 2026 – Present
Machine Learning
June 1, 2026 – Present
Saas Tools
Coursera
June 1, 2026 – Present
Google Data Analytics
Coursera
June 1, 2026 – Present
Gen Ai
Coursera
June 1, 2026 – Present
Python
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio demonstrates a strong alignment with a Lead Data Scientist role, particularly in advanced AI/ML applications. The projects are diverse, covering financial modeling, natural language processing, and multi-agent systems, indicating a broad skill set and intellectual curiosity. The certifications in various cloud platforms (AWS, Azure) and data science tools show a proactive approach to learning and staying current with industry trends. The academic background in Data Science and Information Technology further reinforces a strong fit for a data-driven, technically advanced environment. The candidate's experience, though limited in duration, shows exposure to different aspects of the ML lifecycle, from data preparation to model training and deployment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong ability to translate complex problems into structured solutions, suggesting good analytical and problem-solving skills. The focus on quantifiable outcomes (e.g., prediction reliability, latency reduction) points to a results-oriented approach. The diversity of projects (finance, legal, multi-agent systems) suggests adaptability and a broad interest in applying AI, which could contribute positively to team dynamics and innovation. However, without direct interview data, assessing collaboration, stress handling, and communication in a team setting is limited.