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Risk Advisory @ KPMG | IIT Bombay
I work in Financial Risk Management, with experience across credit risk scorecards, VaR models, and bond valuation models. My focus is on making risk models more reliable by evaluating their performance, stability, assumptions, and regulatory alignment. I enjoy working at the intersection of risk, statistics, AI, and data-driven decision-making. Previously, I completed my M.Sc. in Applied Statistics and Informatics from IIT Bombay, where I built a strong foundation in statistics, machine learning, and quantitative problem-solving. I also interned at Bluepond AI, where I worked on developing GenAI solutions for the insurance domain. I am passionate about applying statistical rigor and AI to solve complex business problems and build transparent, data-driven solutions. Always open to connecting with professionals in AI, data science, credit risk, and financial analytics to learn, exchange ideas, and collaborate.
WorldQuant University
Master of Science - MS, Financial Engineering
January 1, 2026 – Present
Indian Institute of Technology, Bombay
Master's degree, Applied Statistics and Informatics
N/A – Present
Gurukula Kangri Vishwavidyalaya
Bachelor's degree, Mathematics and Computer Science
N/A – Present
KPMG
Associate Consultant
June 1, 2025 – Present
Mumbai · On-site
BluePond AI
Data Scientist
April 1, 2025 – May 1, 2025
Chennai · On-site
Generative AI with Large Language Models
DeepLearning.AI
June 25, 2026 – Present
Deep Learning Specialization
DeepLearning.AI
June 25, 2026 – Present
Deep Learning Fundamentals Certification
Lightning AI
June 25, 2026 – Present
Big Data with PySpark
DataCamp
June 25, 2026 – Present
MLOps Fundamentals
DataCamp
June 25, 2026 – Present
Python 3 Programming
Coursera
June 25, 2026 – Present
Introduction to SQL
DataCamp
June 25, 2026 – Present
Data Collection and Processing with Python
Coursera
June 25, 2026 – Present
Cultural Fit Analysis
The candidate's background in both financial engineering and AI/ML projects (risk analytics, generative AI, NLP) suggests a versatile profile. The diverse set of certifications indicates a proactive learning attitude, which aligns well with a dynamic tech environment. The target role of ML Engineer is well-aligned with the candidate's educational background and project experience, particularly in LLMs and data processing. The breadth of skills and project types indicates adaptability.
Soft Skills & Operational Fit
The candidate's experience descriptions highlight problem-solving (e.g., optimizing PDF ingestion, fixing NLP issues) and analytical rigor (model validation). The project work suggests an ability to work independently and deliver tangible results. However, direct evidence of collaboration, stress handling, or specific communication styles is not explicitly provided in the given data.