Assistant Engineering

SUMIT SETHI

*****.*****@***.*** 812-***-**** https://www.linkedin.com/in/sumitmahaveersethi https://github.com/marvic24 EDUCATION

NEW YORK UNIVERSITY, TANDON SCHOOL OF ENGINEERING Brooklyn, NY Master of Science in Financial Engineering, GPA: 3.7/4.0 Expected 05/21 UNIVERSITY OF PUNE Pune, India

Bachelor’s in Computer Engineering, CGPA: 8.0/10.0 05/17 PROGRAMMING / TECHNICAL SKILLS / CERTIFICATIONS

• Certifications: FRM Level 2, Bloomberg Market Concepts, Parallel and Concurrent Programming (LinkedIn)

• Languages: C++, C#, R, Python, Excel VBA, SQL

• Tools/IDE: GitHub, Perforce, Atlassian Jira, Microsoft Visual Studio, PyCharm, MySQL Workbench, Eclipse IDE. COURSEWORK HIGHLIGHTS

• Mathematics and Statistics: Quantitative methods in Finance, Time series analysis, Algorithmic portfolio Management

• Finance: Derivatives Securities, Risk Management, Valuation and Corporate Finance, Fixed Income securities

• Programming: Financial Computing, Financial Software Lab, Data Structures, Object Oriented Programming EXPERIENCE

NEW YORK UNIVERSITY, New York

Graduate Teaching Assistant - Machine Learning for Finance 09/20 - 05/21

• Guided 30 students on assignments, coursework, and project on supervised, unsupervised, and deep learning techniques.

• Created and maintained a class forum to host discussions between students on emerging trends in Machine learning.

• Acted as liaison between professor and students, communicating student concerns with the professor Graduate Teaching Assistant, Algorithmic Portfolio Management 09/20 - 05/21

• Guided class on R programming libraries like RCpp, RcppArmadillo, RCppParallel used for coursework.

• Resolved student queries on topics including Time series analysis, Active investment strategies and performance matrices. FIS SOLUTIONS (INDIA) PRIVATE LIMITED, Pune, India Financial Software Developer, Adaptiv 06/17 -10/18

• Devised Task Distribution Framework for distributed calculations, reducing calculation time of large portfolios by 60%.

• Evaluated and Implemented product enhancements for newer Financial Risk regulations including FRTB and SA-CCR.

• Improved Continuous Integration (CI) tests functionality in market data module to increase the CI coverage by 50%.

• Built a utility to generate comprehensive report on available and missing CI coverage. BMC SOFTWARE, Pune, India

Project Intern, Cloud Lifecycle Management 08/16 -05/17

• Developed simulator for process creating virtual machines (BladeLogic Server Automation (BSA)), resulting in reduced testing time of Cloud Lifecycle Management (Requests virtual machines from BSA) process from 1.5 hours to 10 minutes.

• Analyzed and documented required output of REST API’s fired by CLM to BSA to optimize REST calls. ACADEMIC PROJECTS

NEW YORK UNIVERSITY, TANDON SCHOOL OF ENGINEERING

Non-Parametric Feature Engineering for machine learning 05/20 - 08/20

• Implemented a low latency R library for calculating non-parametric estimators using C++ and parallel programming.

• Benchmarked library against existing R libraries concluding performance improvement of at least 100%.

• Validated better bias-variance tradeoff with Non-Parametric estimators compared to standard estimators over time series data. Differentiating partisan portfolios using volatility modelling, clustering, and Factor analysis 01/20 - 03/20

• Identified different reaction of portfolios to election by clustering Twitter key words and impact on conditional VaR of GARCH.

• Examined the differences in the partisan portfolios through rigorous factor analysis on portfolio returns.

• Designed structured notes (T-Notes and basket option) to maximize payoff given election results and protect principle. Identifying suitable pairs for Pair Trading using machine learning 12/19 - 02/20

• Determined latent factor loadings using PCA followed by clustering using DBSCAN to get suitable candidate pairs.

• Visualized high dimensional data using T-SNE algorithm in 2D space for validation of pairs. UNIVERSITY OF PUNE, Pune, India

Face Recognition System

07/15 - 10/15

• Created face recognition system using feature extraction methods and ‘PCA’ algorithm in python with OpenCV libraries.

• Tested system on Yale Face Database containing 165 grayscale images in GIF format of 15 individuals with 87% accuracy. HONORS & AWARDS

• Winner - IAQF paper Competition, 2020 (Team Bobcat)

• NYU merit scholarship, 2019

• ‘Kudos’ award for exemplary work on Task Distribution Framework, FIS Solutions, 2018

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