Civil Engineering Python

KAUSIK SARKAR

Harrison, NJ ***** Phone: +1-551-***-**** Email: **************@*****.*** https://www.linkedin.com/in/kausik-sarkar-4206624b/ Education

Rutgers Business School - Newark and New Brunswick, New Jersey Master of Quantitative Finance [Aug 2019 – May 2021] Courses: Optimization Models in Finance, Econometrics, Analysis of Fixed Income, Derivatives Pricing, Object Oriented Programming

(C++ and Python for Finance), Stochastic Calculus, Financial Time Series, Business Analytics and Programing National Institute of construction Management and Research - Pune, India MBA in Construction Management - Finance [July 2014 – April 2016] Courses: Managerial Accounting, Statistics, Economics, Project Formulation and Appraisal, Financial Management, Risk Management and Insurance, Financial Services, Institutions and Markets Jadavpur University – Kolkata, India

Bachelor of Civil Engineering [July 2009 – June 2013] Courses: Differential Calculus, Vector Calculus, Sequence and Series, Numerical Analysis, Project Management Employment

Quantitative Research Intern Basis Point Global Solution Florida, USA [August 2020 -Present]

• Optimized operation cost by integrating Extraction, Transformation and Loading datasets in Amazon AWS using S3, Sagemaker and RDS

• Computed and loaded columns to an existing dataset using pandas, numpy and scipy in Python in Amazon Sagemaker

• Performed descriptive analysis to understand the conditional distribution of each stock by developing Python pipeline to download intraday (30 mins) candlestick stock market data from Interactive Brokers (IB) and EOD data using Refinitiv Eikon workspace

• Automated end of day trading by developing Python scripts to integrate trading strategy with TWS Terminal using TWS API from IB

• Optimised trade execution by tracking the liquidity characteristics of specific stocks at different time intervals of a trading day

• Developed tail risk hedging strategy using options by using Hidden Markov Model and applying Baum Welch algorithm

• Developed portfolio hedging strategy by generating negative beta using options, fixed income derivatives, ETFs and low beta equities Financial Data Analyst Volunteer M4A Foundation California, USA(Remote) [July 2020 – Present]

• Created 5 different cryptocurrency index by emulating S&P 500 from a total set of 39 crypto tokens and calculated the annualized return, daily price std and Sharpe ratio on a real time basis

• Finalized 39 crypto tokens after performing market manipulation potential of 115 tokens using web scraping wallet data from Etherscan, APIs from coinmarket.cap to collect frequency of transactions and percentage holding of different coins of individual wallets

• Predicted market manipulation potential of a token by developing machine learning models and using Natural Language Processing to analyze comments from different websites

Co-adjunct Lecturer, Management Information Systems Rutgers Business School – Newark, New Jersey [Sep 2019 – May 2020]

• Taught and mentored students in Relational databases, SQL, MS Access and MS Excel, graded assignments, Mid Term and Final exams

• Teaching assistant-Finance, Undergraduate and Executive MBA for corporate finance, financial intermediaries and accounting principles Project Finance Manager Tata Steel Ltd. - Tatanagar, INDIA [Aug 2016 – Aug 2019]

• Optimized project risk by programming sustainable SQL scripts for Data Extraction, Transformation & Loading (ETL)dataset to dashboards

• Achieved 15% reduction in procurement cost by increasing efficiency of bidding process by gathering price data of different construction materials and optimizing the maximum bidding price for Blast furnace and Coke Oven project using Python

• Increased project efficiency and employee productivity by 20%after automating the data validation process by establishing an independent access system through the database and servers thereby reducing the team’s data requirements on various cross functional group

Academic Projects

Algorithmic trading model using Machine Learning and High Frequency data Python Programming [Jan 2020 – May 2020]

• Increased accuracy of algorithmic trading model by creating Artificial Neural Network architecture for predicting stock prices of S&P 500

• Increased efficiency of algorithmic trading model by creating features using trend and momentum indicators and fitting them into a combination of classifiers to predict the basis (difference of stock and future prices), back testing and generating trading signals based on the predicted model

Developed portfolio management app for tracking Cryptocurrency and Stock Python Programming [Jan 2020 – May 2020]

• Increased customer satisfaction by developing a portfolio management app in Python for tracking a portfolio of Cryptocurrency or Stock investments and generating alerts for closing of positions based on the criteria set by user XIC CDS Equity Index ETF project [Jan 2020 – May 2020]

• Created Inverted CDS Equity Index for investors who seek to hedge against equity with credit risk or those who seek credit risk exposure Developed model for Credit Risk Management of Banks Python programming [Sep 2019 – Dec 2019]

• Identified the key drivers of Credit Risk for banks by developing a model for predicting the default probability of home and auto mortgages according to CECL guidelines and optimized the model by using Machine Learning algorithms

• Stress tested the model according to Dodd Frank Act Stress Test by estimating the Default probability, Expected Exposure, Loss Given Default, Credit Valuation Adjustments of customers under different scenarios by changing the parameters of the model viz age, city, term of loan, interest rate, total amount of loan, delinquencies, debt to income ratio, loan to value ratio and also by performing sensitivity analysis of model assumptions

• Checked statistical significance of the variables by performing OLS regression after joining the data of different features using pandasql

• Checked autocorrelation by performing L-Jung Box test and multicollinearity using Variance Inflation Factor test (vif)

• Predicted the probability of default by using Random Forest, Gradient Boost, XGBoost, K-Neighbour, SVM classifiers Certifications and Achievements

• CFA Level 2 candidate, FRM Level 1 candidate

• Granted Merit Scholarship in MBA Semester I, II and III, VI, VII by National Institute of Construction Management & Research

• Achieved Pricing Analytics for Corporate Finance Certificate, Quantitative Risk Management in Python Certificate

• Awarded Financial Modelling and Valuation Analyst Certification from Corporate Finance Institute

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