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Data Analyst

Location:
Noida, Uttar Pradesh, India
Posted:
September 17, 2019

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Resume:

PRAGYA SINGH KUSHWAHA

E: adadih@r.postjobfree.com

M: +91-987*******

LinkedIn:

www.linkedin.com/in/pragyakushwaha

EDUCATION

PGP Data Science 2019

Praxis Business School 5.32/8.0

BA Honors in Mathematics 2014

Ambedkar University Delhi 60%

XII (CBSE) 2011

Khaitan Public School, Noida 74.60%

X (CBSE) 2009

Khaitan Public School, Noida 88%

SKILLS

Python Advanced Excel

SAS Data Modelling

Tableau Data Mining

Text Analytics SQL

INTRA-COLLEGE

COMPETITION

(Secured 5th rank out of 30)

2019

Worked on a retail dataset to predict

the probability of a customer churning.

Missing data was imputed and feature

engineering was performed to extract

new relevant features. Final model

applied was XGBoost after hyper tuning

the parameters and predicting the

probabilities of test data.

EXPERIENCE

HSBC, Bangalore 22nd April to Present

- Intern (Credit Card Risk Strategy)

Job Responsibilities

- Origination and portfolio management of HSBC credit card customers. Develop credit card strategies for risk management and profit gain using internal customer database.

Aon Hewitt, Gurgaon – 19th June 2014 to 30th June 2017

- Senior Analyst (3 months)

- Analyst (12 months)

- Junior Analyst (15 months)

o Worked in the Health and Benefits Team, catering to USA based clients

- Intern (6 months)

Job Responsibilities

- Operated independently with strong domain knowledge US Health Benefit with multiple clients

- Conducted Quality assurance/audits for the clients

- Good Knowledge of Health benchmarking, incentive programs (short term/ long term)

- Responsible for stakeholder management (including status updates on project/activities, query resolution, risk documentation etc.)

- Responsible for coaching and mentoring of Jr. analysts in the team

- Management reporting

PROJECTS

PUMP IT UP (DECEMBER 2018)

Data Source: DrivenData Tools Used: Python

Problem Statement: To predict using data from Taarifa and the Tanzanian Ministry of Water, which water pumps are functional, which need repair, and which don’t work at all. Approach: Preprocessing the data and performing descriptive analysis & extensive EDA to understand and gather surface level insights. Extracting important features and running supervised classification models such as decision tree, random forest, gradient boosting and bagging classifiers. Highest accuracy achieved was 79% with gradient boosting classifier.

CAPSTONE PROJECT- STOCK PRICE FORECASTING (APRIL 2018) Data Source: National Stock Exchange of India Tools Used: Python Problem Statement: Use minute-wise data of 4 stocks and forecast the stock prices for the next week

Approach: Gathering minute-wise stock prices via web scraping, using ARIMA and ANN models for time series analysis on the data and using Dash as a web-app for making a front-end dashboard.



Contact this candidate