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Lessons offered by Anmol
  • Individual
The lessons will be held
Taught subjects
  • Computer programming
  • Computer languages
  • Python
  • Artificial intelligence
  • PostgreSQL
  • All Levels

Senior Data Scientist with overall 5+ years of professional experience can help students in their assignments/homework/concepts related to Data Science, Machine Learning, Mathematics, Statistics, Prob


To help students giving examples at every step. I use white board, code editors and text editors to help students visualize the concept. But the approach is very subjective in nature depending on how the student would like to have it.


I am a Data Scientist working with world's top retailer currently. I have graduated from IIT Kharagpur with Masters in mathematics and Computing. IIT Kharagpur is one among the top 5 premier institutes in India.

As a tutor, I have worked with chegg.com for about 5 years and have helped about 2000+ students in 5000+ assignments so far in Python, R, SAS, Statistics, Machine Learning and Data Science domain


Rate for online lessons : $15/h
Rate for 5 hours of lessons : $75
Rate for 10 hours of lessons : $150
Lessons offered by Anmol
The lessons will be held
Taught subjects
  • Computer programming
  • Computer languages
  • Python
  • Artificial intelligence
  • PostgreSQL
  • All Levels

Anmol's resume


Year Institute Qualification CGPA/%
2010-15 IIT Kharagpur Integrated M.Sc. in Mathematics and Computing 8.06
2008-10 Narayana Jr. College, Hyderabad Intermediate Public Examination (I.P.E) 97.9%
2007-08 Montessori High School, Yellandu Secondary School Certificate (S.S.C) 88.3%

WORK EXPERIENCE: Total Experience: 5.5 years
Amazon - Retailer (Sep’20– Present)
Data Scientist II – Individual Contributor:
Pricing Strategy (Markdown Optimization): Tools: Python, Amazon Redshift, SQL
• Part of 3 membered DS team that built end-to-end solution using price elasticity and Non-Linear programming problem to solve the problem of recommending optimal price to markdown so that left-over inventory sells off
• Involved clustering, forecasting, price and demand curve pattern analysis and optimization as four individual components in the entire process where clustering and ML algorithms are used while building these modules
• Annual saving estimation through the project is around $11-15M every year
• Project went into production at test stores for a sample of departments
L Brands Inc. Bangalore (VS, Pink, Henri-Bendel, Bath & Body works) – Global Retailer Fashion Industry (Nov’17 – Jul’19)
Data Scientist – Individual Contributor:
Voice Integration Project (PoC project): Tools: Python, R shiny, SQL, Teradata
• Built a voice based analytical tool from scratch using an integration of R Shiny, Python, Teradata SQL and a javascript based voice recognition package. Python is used for the core work in the backend
• This tool recognizes human voice using js and converts into text. Algorithms are setup in python for converting text to SQL (NLP to SQL), which then hits Teradata and fetches the output. Presented this in Columbus, Ohio - Oct’18

Digital Analytics Team(Customer Analytics): Tools: HiveQL, Python-Keras, SAS EG, SAS EM
• Image Recognition: Built a recommendation algorithm to fetch the most matching product images in the website when an image is dropped by the user using Keras in python (Ongoing)
• Optimization of codes: Leveraged the multi-processing module of python to reduce the run time of various codes in MapR environment.

EXL Service Pvt. Ltd, Bangalore(2 Years and 3 Months Experience) (Aug’15 – Nov’17)
Consultant- II: Predictive Modeling Team (Machine Learning) : Worked for a mass media giant company from USA

Built the following predictive models: Tools Used: SAS EG, SAS EM, Excel

• Upgrade Models:
(a) Binary logistic model in SAS Enterprise Guide to predict the customers who has higher probability of upgrade to high internet speed tier. Validation on Out-of-time dataset beating benchmark model.

(b) Using neural network in SAS Enterprise Miner to find out people most likely to upgrade to digital video recorder service in next three months.

• Customer Targeting Models:
(a) Using artificial neural network in SAS Enterprise Miner for forecasting the probability of customers more likely to use client’s app to perform tasks which include bill payment, change settings like password settings and other end user services.

(b) Built model to identify customers likely to have android vs iphone mobile to understand the kind of updates the apps needed to focus on.
(c) Built model to identify who all prospects are more likely to take product with combination to a competitor’s product which comes as a package.

• Risk/Churn Models:
(a) Predicting amount of delinquency of customers at the end of 3 months using linear regression as a base model and then optimized the results using neural network and other machine learning techniques. Platform used is SAS Enterprise Miner.
(b) Segmentation of customers: Using K Means Algorithm, identified natural clusters in the data and gave business meaning to each cluster based on the delinquency and churn rates across different segments.
• Publication: Algorithm for GMP1R with Single Hole, IEEE Proceedings of SCES-2014, MNNIT Allahabad, May 28-30, 2014
• Achieved 8th rank mark wise and 22nd rank overall in State EAMCET Exam among ~311,000 students in 2010
• Ranked within 0.74% in Indian Institute of Technology – Joint Entrance Examination 2010
• Acquired AIR 272 in All India Engineering Entrance Examination, 2010 among ~110 thousand applicants
• Cracked Birla Institute of Technology and Science Admission Test (BITSAT) with an aggregate mark of 368
• Received INSPIRE Scholarship consecutively for five academic years, 2010-15
• Advanced Python, Advanced SAS(Expert Level), R, SQL, HiveQL, VBA, RedShift SQL
RCI, Ministry of Defense, Hyderabad (May’14-Jul’14)
Internship in Machine Learning:
End-to-End coding of below algorithms
• K-Means Clustering: Developed a code in RCPP platform and tested it on R. Obtained a much efficient code with reduced time complexity which uses centers convergence as stopping criterion.

• KNN: Coded in RCPP. End-to-End algorithm was coded that checks the best k value which can minimize misclassification rate. Got an accuracy of 96.2% overall when tested on test dataset

• Support Vector Machines: Linear SVM was developed in R without using any default functions

• C- Means Fuzzy: Built complete code that takes input dataset and c value. The classification obtained from c-means fuzzy was better than inbuilt function of K-Means

• Maximum Likelihood: Wrote it in R and tested on it against the original labels. Obtained an accuracy of 82.3%
IIT Guwahati Under Prof. Kalpesh Kapoor, Department of Mathematics (May’13-Jul’13)
Internship in Design and Analysis of Algorithms:
• Presented an algorithm of O(n3) to solve Graph Motion Planning involving one Robot(GMP1R) problem for undirected graph
• Implemented the concept of configuration graph and solved the problem by ‘Breadth First Search’ and ‘Meta Graph Search’ algorithms. The algorithm was further extended for directed and weighted graphs as well
• Link: (concealed information)

Competitions and other:
Chegg Tutoring: (Jun’14-Aug’16)
• Taught many students in subjects like SAS, Python, Machine learning, R, SQL and graduate level Mathematics courses
• Guided a lot of US based students in their Master’s and Ph.D thesis especially related to statistics and Machine Learning
• Maintained 99% overall rating in the profile with over 650+ ratings and 1500+ hours of tutoring
• Profile Link: (concealed information)

EXL Excellence Qoutient-2014 Modelling Competition (Sep’14-Dec’14)
• Developed forecasting model using Linear regression and predicted spot LHR
• Model provided range of LHR to give flexibility to Account Managers and Expected ROI were calculated from this range
• Ended as Runner Ups among 750 total teams participated all over IITs to win cash prize of 50,000 INR
• Link: (concealed information)

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