SAHIL CHADHA

Enthusiastic and results-driven professional with a background in R&D Mechanical engineering, I transitioned my career to align with my passion for mathematical modeling and data science. Currently pursuing a Master's in Data Science, I am also engaged in an additional online course in Quantitative Finance with World Quant University to further enhance my skills. My keen interest lies in the intersection of data science and finance, particularly in time series modeling, where I am exploring the profound impact data science can have on quantitative finance and algorithmic trading. Eager to contribute my diverse skills and knowledge to innovative projects at the nexus of technology and finance.

• Ranked 3rd in Masters in Quantitative Economics entrance examination, conducted by Indian Statistical Institute
• Department Rank in top 5 amongst 73 students in Mechanical Engineering B.Tech programme, IIT Delhi
• Ranked 2nd - 'NetworSify By OPTIMA, 23' (IIT Kharagpur)
• Ranked 4th - 'Flipkart Product Launch Challenge, 23'
• National Finalist - 'Fin-A-Lytics by OPTIMA, 23' (IIT Kharagpur)
• National Finalist - 'IDB Analytics 3.0' (IIM Calcutta)
• JEE MAINS: Secured an All India Rank of 360 in JEE Mains, 2017 among 1.1 million students who appeared in it
• KVPY scholar: Secured All India Rank 1341 in Kishore Vaigyanik Protsahan Yojna conducted by IISC, Bangalore, 2016
• Summer Undergraduate Research Award: Awarded for research project done in summer 2019 by IRD, IIT Delhi

Global Wheat Price index Forecasting

• Analyzed monthly 13 years Wheat Price data and Forecasted the transactions for the next few month using SARIMA modelling with Best AIC & BIC score
• Checked Stationarity condition using ADF test, analyzed ACF and PACF plots and performed residual analysis and Ljung-Box test.

Tool wear Prediction of Micromilling process (Btech Project, IIT Delhi) [Aug, 2020 -Dec, 2020]

• Prediction of tool wear given the microscopic image of micro-Milling tool using image processing and Machine learning techniques
• Real time prediction of Remaining useful Life of Tool using Sequential learning techniques like LSTM and GRU

Member of Sponsorship Team in Conclave team of PGDBA, Batch 9

Stochastic Processes and Applications, Statistical Structures in Data, Statistical Inference, Deep Learning, Regression and Time Series Modelling, Financial Market, Financial Data.