1. SHAILESH KUMAR
H-104, Jalvayu Vihar, Sector-20, Kharghar, Navi Mumbai-410210
DOB: 6th
Oct 1973, Phone: +91 702 186 3053, +91 9920 680 760
E-mail: Shailesh.mslal@gmail.com, shailesh.mslal@outlook.com
PROFILE AT A GLANCE
 M.Sc. in Mathematics from Indian Institute of Technology, Delhi.
 Around 17 years’ of experience, that includes around 9 years of experience in the area of Quantitative
Modeling, Statistical Modeling, Statistical Analysis, development in Python and 7 years of teaching
experience in Mathematics, Statistics and Operation Research at postgraduate level.
 Strong knowledge of advanced statistical methods, Bayesian learning techniques, pattern recognition and
machine learning algorithms
 Strong knowledge of statistical software R,MATLAB, NUMPY, SCIPY, NLTK, SCIKIT-LEARN, ELASTICSEARCH
 Possess an inquisitive & analytical mind, creative thinking, excellent organizational skills, flexible & result
oriented attitude combined with strong analytical & information analysis skills and a proven ability to interact
with a diverse range of people in a professional manner.
PROFESSIONAL EXPERIENCE
Banque Nationale de Paris PARIBAS, (BNP PARIBAS), Mumbai
Associate, (July 2007- Present)
As a member of statistical arbitrage team (equity and derivative), day to day activity is to participate in
quantitative research activity, data analysis, designing and developing statistical model and verifying the models
on historical data. Some of the projects completed in BNPPARIBAS:
 Document and Sensitivity analysis using machine learning: BNPPARIBAS publish research documents for
it clients and sales people. Objective is to extract entities from documents, calculate sensitivity of
document, find market sensitivity of entity and send research document to client and sales people when
at right time. Using NLTK and Scikit-learn module.
 Stochastic Volatility: I was part of team (size 2) which developed stochastic volatility model by pricing
variance swap.
 Stochastic correlation: Developed stochastic correlation model by pricing correlation swap and written
script in python.
 Pair Trading strategy : when two stocks moves together for long time and deviates some time, then we
can exploit these kind of stocks buy taking long position in one stock and short position in another stocks.
Used Co-Integration method and Implemented and tested different mean reversion technique to decide
when to take or close the position. Developed the strategy for US and Europe Market
 Low Rank Correlation Matrix Optimization: There are two problems with correlation matrix. There is
stochastic noises in the correlation and if correlation between two stocks is zero or near to zero then
there is no use of this correlation in correlation matrix. Objective is to find the correlation matrix with
reduced stochastic noise and reduced rank of the large dimension (~ 15,000).
 Replication of Eurozone index: Objective of this project was to create a basket for Eurozone index,
because we cannot directly buy/sell indexes. Created basket for Eurozone Index using Regression
Analysis using Excel, VBA and python
 Long Call and Long Short volatility index (Eurozone equities): If we want to include these position in our

2. portfolio, then we should know the past performance of these indexes. Implemented these indexes for
several strategies.
 Sensitivity of a equity trading book : Calculated the daily volatility of a some equity trading book
( collection of different positions in different stocks and options)
 Impact of macroeconomic news on SPX return: We all know there is impact of news on indexes. Used
Volatility analysis to check the impact on SPX return.
 Realized intra-day variance: Calculated realized intraday variance as a function of time for European and
US Market. It helps us to decided, when to take the positions in the market.
 Analyzed (Quantitatively) Impact of Big Intra-day trade for European and US Market.
 Analyzed mid volatility and smile for European and US Market using Excel, VBA and python.
 Designed the strategy based on arbitrage between ADR and its underlying securities for European and US
Market.
 Analyzed liquidity of equities and option for Indian Market
 Derivatives Market data for GECD Compliance Simulations: Intraday and End of Day market data is
needed for compliance simulations. Reuters used as intraday day market data source for futures and
warrants. Opra used as intraday market data source for US options. Bloomberg is used as source end of
day market data. Involved in development of systems to provide market data for US, Europe, and Asian
derivatives.
 Market data for Fixed Income Compliance Simulations: Intraday and End of Day market data for needed
for compliance simulations. Involved in development of systems to provide intraday and EoD market data
for Bonds from all regions.
 Equities Market data for GECD Compliance Simulations: Intraday and End of Day market data is needed
for compliance simulations. Reuters and Bloomberg are used as source of intraday and end of day market
data respectively. Market data is provided for BNP Paribas positions all over the world. Involved in
development of systems to provide market data for US, Europe, Asia and African Equities.
Riskraft Consulting Ltd, Chapala, Mumbai
Software Engineer (Aug 2006-june 2007)
 Credit Risk: Implementation of standardised and IRB approach, rating methodology, computation of PD,
LGD and EAD and capital computation. The implementation of IRB methodology included study of data
gap analysis, rating design, system requirement and implementation, management & regulatory reporting
and capital computation.
 Market risk: Implementation of VaR methodologies, capital computation, limit management & reporting
and ALM
 Operational Risk calculator where Markov chain Monte Carlo was used.
 Margining System: While working on Riskmetrics I was partially involved with a small project on
Margining System for commodity market. The part I worked on was to write a GUI and internal code in C+
+ which can automatically fetch data (spot and future prices) from internet sources like mcxindia.com,
ncdex.com etc and append it to database.

3. Institute Of Management Studies, Dehradun
Sr. Lecturer (Mathematics, Statistics) (Aug-1999- Aug 2006)
 Teaching Master of Business Administration (MBA) and Master of Computer Application (MCA) courses.
The subjects I taught were
 Numerical Methods
 Graph Theory
 Financial Mathematics
 Probability and Statistics
 Differential Equations
 Linear Algebra
 Computational Usage and programming with C++
 Optimization Techniques and Linear Programming
 Preparing study materials and computer based tutorials for various courses. I prepared lecture notes and
study materials for coming semester courses. These were from mathematics and computer sciences
topics.
 Guiding Master in Computer Application (MCA), and Master in Business Administration (MBA) students on
their projects.
 Sports-in-charge
Education:
 Master of Science in mathematics from (IITD) Indian Institute of Technology, New Delhi, India in 1999.
 6-month Certificate Course in Drug Designing from GVK Bioscience, Hyderabad
 6-month Certificate Course in C++,Java from TATA InfoTech, Hyderabad
Additional
 Student Member Actuarial Society of India, Mumbai
 Relocation : Mumbai, Delhi, Noida, Gurgaon, Bangalore, Chennai, Hyderabad, International
 Notice period: Three months ( negotiable)
 Expected CTC: Negotiable
Shailesh Kumar