AI Engineer with 1+ years in Data Analysis & Machine Learning.
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Analytical and execution-oriented professional with ~1 year of full-time experience driving data-led business decisions at Willis Towers Watson, plus hands-on AI/ML project work. Skilled at translating complex datasets into clear stakeholder narratives, building performance dashboards, and streamlining operations. Quick learner with a strong academic foundation from Hansraj College, University of Delhi – comfortable in fast-paced, ambiguous environments and eager to bring structured thinking to Aramya's cross-functional growth agenda.
Department of Computer Science, University of Delhi
Master of Science · Computer Science
August 1, 2024 – June 30, 2026
Hansraj College, University of Delhi
Bachelor of Science (Honours) · Computer Science
August 1, 2020 – June 30, 2023
Defence Research and Development Organisation (DRDO) - SSP Lab
Research Intern
June 1, 2025 – July 1, 2025
Delhi, Delhi, India
Willis Towers Watson
Actuarial Analyst
June 1, 2023 – March 1, 2024
Gurgaon, Haryana, India
Sales Data Analysis
June 1, 2026 – Present
Analyzed 185K+ transactions and built interactive Power BI dashboard tracking KPIs across products and regions. Identified top revenue drivers and growth opportunities, providing actionable insights for business stakeholders to improve performance.
View ProjectFinancial Fraud Detection
June 1, 2026 – Present
Developed a fraud detection machine learning model on 6.3M+ financial transactions achieving high fraud recall through engineered balance-risk and transaction-pattern features to flag high-risk transfer and cash-out activity. Identified key fraud drivers and proposed real-time monitoring and risk-alert thresholds to reduce financial loss.
View ProjectAirline Passenger Satisfaction
June 1, 2026 – Present
Performed end-to-end exploratory data analysis (EDA) and predictive modeling to assess customer satisfaction, achieving 96.2% accuracy using a Random Forest classifier. Conducted EDA and visualized key factors impacting customer satisfaction to support data-driven business decisions, presenting findings via an interactive Streamlit dashboard.
View ProjectOffensive Humour Detection on Social Media
June 1, 2026 – Present
Built a data pipeline for offensive humor detection targeting disabled individuals in Hinglish Instagram comments using multi-stage text preprocessing and a curated 10,000-comment multilingual dataset from 214,360+ raw scraped comments Benchmarked machine learning models across 4 paradigms, achieving best result of 87.8% accuracy and 0.88 macro-F1 with Random Forest, demonstrating ensemble superiority on low-resource code-mixed data.
Quantitative Research Virtual Experience Program
JPMorgan Chase & Co.
June 1, 2026 – Present
Introduction to Data Analysis
IBM (Coursera)
June 1, 2026 – Present
NFL Big Data Bowl 2026 (Competition)
Kaggle
June 1, 2026 – Present
Artificial General Intelligence
Bitwise, Annual Tech Magazine of Hansraj College
June 1, 2026 – Present
Best UI/UX Award
MLH Hackathon 'Design-a-thon'
June 1, 2026 – Present
Certification Course in German
Hansraj College, University of Delhi
January 1, 2021 – Present
Cultural Fit Analysis
The candidate's diverse project portfolio, including financial fraud detection, airline passenger satisfaction, and offensive humor detection, indicates a broad interest in applying AI/ML to various domains. Their academic background in Computer Science and pursuit of a Master's degree align well with a technically driven culture. The 'AI Engineering Fellow' experience and participation in hackathons and competitions suggest a proactive and growth-oriented mindset. The 'Actuarial Analyst' role, while not directly AI, shows experience in data processing and stakeholder communication, which are transferable skills. The overall profile suggests a good cultural fit for a role that values continuous learning, problem-solving, and practical application of AI.
Soft Skills & Operational Fit
The candidate demonstrates strong analytical and problem-solving skills through their project work and professional experience. Their ability to collaborate with cross-functional teams and present findings suggests good communication and teamwork. The mention of being a 'quick learner' and comfortable in 'fast-paced, ambiguous environments' indicates adaptability and a proactive work attitude. However, without specific psychometric test results, a deeper assessment of stress handling and team collaboration is limited.