Dattatreya Biswas has a PGDBA from IIM Calcutta with a 7.7 CGPA and a B.E. in Computer Science Engineering from PESIT Bangalore South with 71%. He has internship experience at McKinsey Knowledge Centre working on projects in telecom digital and operations forecasting. He also has work experience as a Service Delivery Consultant at Hewlett Packard working on network fault management. He has extensive experience with analytics projects covering topics such as credit risk fraud detection, Apple pricing strategy, bank marketing, startup success prediction, Brexit sentiment analysis, and spam detection. He is proficient in tools including R, SAS, Python, SQL, C, HTML, Table

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PGDBA - Dattatreya Biswas dattatreyab2017@email.iimcal.ac.in
Academics
Degree/Examination Year Board/University %/CGPA
PGDBA 2015-2017* IIM- C, ISI-Kolkata, IIT-KGP 7.7
B.E, Computer Science
Engineering
2010-2014 PESIT Bangalore South 71
Class XII 2010 CMR National PU College 80.1
Class X 2008 CMR National Public School 89.2
PGDBA Industry Internship
Intern McKinsey Knowledge Centre, Gurgaon Nov 2016 – Present(Completes on April 2017)
Telecom Digital
Sprint
 Worked in customer life cycle management project for potential clients across the telecom industry
 Debugged SAS scripts and utilized SQL scripts to obtain multiple datasets for dashboards
 Automated the diagnostic dashboard using advanced Excel and Tableau to help derive insights
 Built an incipient predictive model using logistic regression to predict churn of postpaid users
Operations
forecasting
 Provide better Supply chain decisions to potential clients using various forecasting techniques
 Created forecasting models using Exponential smoothing and open source prophet for forecasting
Work Experience
Service Delivery Consultant Hewlett Packard, Bangalore Sep 2014 – Jun 2015
 Worked under the HP CMS team for a telecom project for one of the largest telecom service providers in the country
 Worked on network fault management system to minimize downtime of network infrastructure using TeMIP and HPSM
 Collaborated with a team of 3 members to develop Perl scripts for automation and for the upkeeping of various servers
Analytics relevant Projects
Credit risk fraud
 Assessed credit card fraud risk of banking dataset containing 26,000 observations and 32 variables
 Initial descriptive analysis was carried out to figure out the potential variables causing fraud
 Random forest was deployed for higher interoperability and obtained a model accuracy of 80%
Revaluation of Apple
pricing in India
 Redeveloped pricing strategy of Apple iPhone SE in India to increase smartphone market share
 Carried out Conjoint analysis and discrete choice analysis to obtain the most optimal price range
 Obtained customer’s willingness to pay based on optimal price and price of nearest rival phones
Bank Marketing
 Analyzed post financial crisis(2008) UCI dataset of a Portuguese bank for various customer profiles
 Applied DBScan, K-Means techniques to obtain profiles of the customers who made term deposits
 Determine highly probable potential new customers who are likely to create a new term deposits
LatentView Winter
project
 Analyzed the success and failure of around 472 startups and with more than 100 characteristics
 Applied multiple imputation applied models such as Random forest, CART, Logistic regression
 Performance of models were measured using AUC and ROC curves and obtained accuracy of 84%
Brexit Sentiment
analysis
 Collected over 150,000 tweets on Brexit from Twitter to investigate the underlying trending topics
 Used NLP model to calculate the polarity of tweets and build an emotion identifier for the same
 Ascertained the veracity of the analysis in context of prevailing circumstances at that point in time
SMS Spam detection
 Classified over 5574 UCI data text messages as spam or ham using logistic regression and XGBoost
 Feature engineering -upper case characters, spelling error, frequent spam words, special characters
 Achieved a model fit performance of 73.5% in Nagelkerke R-square and 98% accuracy for XGBoost
Automobile Industry
analysis
 Analyzed the automobile industry from social media of four car brands with over 0.8 million data
 Perceptual attributes such as comfort, price, quality, safety was compared for the four major cars
 Analyzed sentiments of four car brands and customer’s expectations across low, medium, high end
‘What’s Cooking?’ –
Cuisine prediction
 Competed in Kaggle as a team to predict cuisine from given ingredients list and web crawled list
 Web crawled using Python Scrapy Gradient Boosting(XGBoost), Random Forest, Naïve Bayes in R
 Obtained an accuracy of 0.791 and best leaderboard rank of 186th out of 1036 participants in Kaggle
Position of Responsibilities/Co-curricular achievements
 Secured a rank of 3 in class 12 in National Science Olympiad and National Cyber Olympiad, CMR National PU, 2009
 Member of core organizing team for organizing technical hackathon(inGenius) of 110+ teams, PESIT Bangalore, 2013
 Member of the organizing team of technical Quiz competition in inter College fest Maaya, PESIT Bangalore, 2011
Technical Skills
 Tools/Certifications –R, SAS, Python, SQL, C, HTML, Tableau, Advanced Excel, Stanford Lagunita SQL certification