Sales Assistant

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CHARLES A

DATA SCIENTIST CONSULTANT

Phone : +91-974*******

Email :

***************@*****.***

Python

R Programming, R-shiny

SAS

SQL, Teradata

Hive,Big Query

Tableau, Power BI

Microsoft Office

Testing of Hypothesis

Linear Regression

Market mix Model (MMX)

Logistic Regression

Decision Trees (CART &CHAID)

Clustering Analysis

Principal Component Analysis

Factor Analysis

KNN (K- Nearest Neighbors)

Random Forest

Support vector Machine

Gradient Boosting

Text Analytics

NLP(Sentiment Analysis)

Retail

Telecom

Media & Entertainment

HR Analytics

Marketing Analytics

Customer Analytics

CAREER OBJECTIVE

Analytics Professional with Seven plus years of experience in Advanced Analytics techniques to solve real world business problems. To work in an environment where I can utilize my Statistics and Mathematics knowledge along with technical skills to provide efficient Business solutions and to solve real life problems, and bring out an impact towards the success of an Organization and my own career growth. DATA SCIENCE CONSULTANT

Accenture, Chennai

2018 to Present

PROJECTS

Product Rationalization on leading Telecom Operator Developed a product rationalization based on transferable demand algorithm to trim the product line with minimal effect on the revenue. Identifying important attribute affecting overall revenue using attribute regression model.

Calculating the transferable demand for each product using the coefficient and those attributes. Retaining the products that contributes more revenue based on its unique attributes.

Business Impact: By dropping down 80% of the products was still able to retain the revenue by just 3% of revenue loss.

Design campaign to migrate customers from low ARPU bucket to high ARPU Developed a predictive model to identify customers having high propensity to Migrate from low ARPU bucket to high ARPU by analyzing historical movements of the customers within different buckets of ARPU. Advanced machine learning techniques are implemented in big data environment using Hive.

Customer Survey Analytics for Telecom Operator

A survey report consisting customer feedback (scores) on different departments

(Sales, Provisioning, Billing etc.) was analyzed to identify key factors influencing Customer Satisfaction Score (CSAT).

Dimensionality reduction approach was used to identify relative importance of each factor.

Text mining techniques were used to understand customer sentiments from customer comments and group them into positive, negative & neutral. Model was built in Python.

Slot ask Model to identify the interview count and demand Developed a model to predict Interview Conduct number & demands to be processed, based on targeted onboard numbers at IG level. Capture the requirement and use it for future reference.

Advanced machine learning techniques (Gradient boosting, random forest, and Decision tree) are implemented and the model was developed in Python.

Skill conversion Model to identify the conversion rate Developed a model to predict the conversion rate at Skill level location. Based on behavioral history of the interviewees in last six month. Advanced machine learning techniques (Gradient boosting, random forest, and Decision tree) are implemented and the model was developed in Python. PERSONAL INFORMATION

TECHNICAL SKILLS

DOMAIN KNOWLEDGE

STATISTICAL TECHNIQUES

Master of Statistics,

Loyola College Chennai

(2010 -2012) -7.7 GPA

Bachelor of Mathematics,

K.R.M.M College Chennai

(2006 -2009) -6.3 GPA

Data Science Consultant at

Accenture 2018 to present

Assistant Manager (Data Science)

at Genpact

2017 to 2018

Statistical & Modeling Senior

Analyst At Accenture

2016 to 2017

Senior Business Analyst at

Tata Consultancy Services

2012 to 2016

Completed Base SAS and

Advance SAS in Complete

Analytics Institute.

Completed Machine Learning,

Linear Algebra, Calculus and

R-programming at Coursera

Completed Python course at

Analytics Vidhya

ASSISTANT MANAGER (DATA SCIENTIST)

Genpact, Bangalore

2017-2018

PROJECTS

Customer Propensity to call Disney reservation center or website visitors Developed a propensity model for better understanding of different booking behavior and pattern of a customer who enquire through Disney reservation center or website visitors of DisneyWorld.COM.

Perform data cleaning and data preparation for further analysis (Missing value, Outlier/Capping, Dummy variable creation) in big data environment using Hive. Build Propensity Model using R and predict the characteristics which are more likely to book the resorts.

Business Impact: Identified 90% of the insignificant variables and booking behavior of the customer.

Market Mix Model

Developed MMX model to understand Market spend effectiveness of different marketing levers to plan for the next Quarter.

Mixed procedure used to develop the MMX Models containing both fixed and random effect.

Business Impact: Allocated spend in better way on the different tactics based on effectiveness of market activities.

STATISTICAL AND MODELING SENIOR ANALYST

Accenture, Bangalore

2016-2017

Responsible for leading, developing and managing all aspects of Data and Analytics – Predictive Modeling, ad-hoc analytics etc.

Providing analytical support, Pricing analytics and promo effectiveness in order to maximize gross margin and minimize discounts.

Quantifying Promo Effectiveness for a major Retailer Quantify sales uplift to price discounts understand impact of other factors on sales.

Business Impact: Provided a holistic view to the business of impact of promotions on sales.

SENIOR BUSINESS ANALYST

Tata Consultancy Services, Bangalore

2012-2016

Data Analytics: Analyzed data based on the reports and implemented Data Mining Techniques like classification and clustering to come up with a better model for the process which resulted in improvised customer satisfaction.

Trend Analysis: Identifying the trend and seasonality effects on different products to understand the behavior of purchase in different channel/regions.

Cluster Analysis: Identify similar group of stores and create similar Market strategy for each group.

Predictive model: Predicting store sales using linear regression. EDUCATION

WORK EXPERIENCE

KEY ACHIVEMENTS

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