Sign in

Engineering Data

Vasant Nagar, Karnataka, India
February 28, 2020

Contact this candidate



Linked +91-979*******

1. Public Company Accounting Oversight 5. Mean Board Absolute 2.Relational Error 6. database Root Mean management Squared Error system 7.Operations 3.Classification & Infrastructure and regression Planning trees 4. L&T Financial Services PROFESSIONAL EXPERIENCE

Associate-2, Data & Analytics team, KPMG

Machine Learning

backed Resource

Allocation Optimization

Jul ’19 - Present

KPMG Automated § § § Optimizing improve Executed parameters Future of KPMG application the by CatBoost, and utilizing resource quality achieved of of XGBoost the this allocation work resources project state delivered algorithms of is the in in to collaboration art an implement to efficient accuracy clients and performed way this with of model the cater 5% a Grid-Quality during to Search all Resource the the needs busy to find season of Management the its best clients (financial hyper- team yearend) to Audit Procedures

Jul ’19 - Present

§ § Performing financial Reviewing audit yearly/PCAOB1 of Data 15+ quarterly compliant Science clients financial Intern procedures statements Okavango on requirement to - IITMRP check for basis risks, using accuracy, SQL RDBMS2 and precision to assist for with all clients Sensitivity Analysis for

High Attrition Rate

Jul’18 – Dec’18

§ Worked bank in a team of 5 to identify the major factors behind the high attrition rate of employees in a

§ Executed dimensionality CART3 of & the Random data by forest capturing classifier the poorly to find correlated patterns features in the attrition with the of help employees. of heat Reduced map

§ Stacked the new model, XGBoost the & bank Random devised forest new and strategies got an accuracy to retain their score current of 0.89. employees With the insights provided by

§ Inseminated and prepared content for the course ‘Data Pre-Processing with R’ by Prof. Gaurav Raina MACHINE LEARNING COMPETITIONS & PROJECTS

KPMG Deal Advisory


Aug ’19

§ Formulated trends using deal predictive advisory analytics models for and clients natural in logistics language and processing operations industry by analyzing deal

§ Achieved state of the art accuracy of 86% by using hyper-parameter tuned random forest

§ algorithm. Placed 4th among Used feature 120 teams extraction, across stemming KPMG India and in ensemble the 2-day techniques long hackathon to pre-process the text data L&T Fin-Hack


Jan ’20

§ § Built LTFS4 Used of 40 a Random on model a daily to Forest predict basis for algorithm the 3 months number with using of hyperparameter country-historical wise data loan of tuning applications 3 years to achieve received an accuracy by 2 business of 70% verticals and MAE5 of House Price Prediction


Jan ’20

§ § Explored Reduced Used Ensemble, features features XG-by using using Boost Lasso correlation and and Random Ridge map regressions Forest to detect models multi to predict to co-predict linearity house house prices & imputed prices with missing RMSE6 values. value of 0.109 and placed in top 1%



§ § SQL Python, R § § Visual MATLAB Studio § § Excel MS PowerPoint Courses

§ § Introduction Introduction to to Machine Data Science Learning § § Linear Multivariate Algebra Data Analysis § § Introduction Introduction to to Python R EDUCATION

Program Institution %/ CGPA Year of completion

B. Minor: Tech. in Industrial Aerospace Engineering Engineering Indian Institute of Technology Madras 5.82/10 2018 XII X ((AP AP State State Board) Board) Chaithanya Narayana POSITION Bharathi Junior OF School, College, RESPONSIBILITIES Madanapalle Nellore 94.92.3% 3% 2013 2011 Marketing-Executive

Team Involve


Head logistics § § § Part peer Led Devised a of to team the peer a structured IITM of learning 12 incubated to mentor program 30 educational to students improve of start-the classes conceptual up 4th aimed to 8th at understanding across empowering 10+ schools of underprivileged students in 2 localities kids in through Chennai Shaastra ( O&IP7 )


§ § § Negotiated Spearheaded Cracked a deal with a EXTRACURRICULAR with team OYO NTL of Rooms 8 Cabs to coordinate & and saved acquired 50K the ACTIVITIES on logistics cabs accommodation at 70% and operations of the and normal amenities of IITM’s price bar technical quotation of 70K fest for Shaastra 500 guests ‘17 Sports

§ § § Captain Received Placed 1st – Gold, Institute in football Silver Tennis among & Bronze team a pool as medals a of part 8 teams; in of Football, National Active Cycling Sports participant Organization, & Athletics of Carom Inter IIT tournament Hostel Madras Competition at KPMG Art & Cultural Activities § § Self-An active taught participant doodling artist in dramatics (Instagram: and short https:film // IIT instagram.Madras and com/KPMG makeiteasypolicy/)

Contact this candidate