Saksham Singh

Phone No: 857-***-****, * B Smith St, Boston, Email: adfpej@r.postjobfree.com

EDUCATION Northeastern University, Boston 2018-2020

Master of Science in Engineering Management (Concentration in Data Analytics) GPA-3.5 Relevant Coursework: Engineering Statistics and probability, Data Mining, Database Management System, Data Warehousing and Business Intelligence, Machine Learning in Finance, Project Management, Economic Decision Making Harvard Business School Online Summer 2019

CORe Credential of Readiness

Relevant Coursework: Business Analytics, Economics for Managers, Financial Accounting Symbiosis Institute of Technology, Pune, India 2011-2015 Bachelor of Technology in Mechanical Engineering

SKILLS

● Analytics: Python, R, Google Analytics

● Database/ Query Writing: Oracle, MySQL, MSSQL, Postgres (T-SQL)

● Business Intelligence/Data Visualisation: Power Bi, Tableau, Excel, Qualtrics

● Data Integration: Alteryx, Talend, SSIS, Informatica

● Statistical Analysis: A/B testing, Hypothesis Testing

● Machine Learning Algorithms: Linear, Multiple and Logistic Regression, Random Forest, Arima, SVM, k-means clustering WORK EXPERIENCE Think & Learn Pvt. Ltd (BYJU’S), Bangalore, India July 2015 - April 2017 Business Data Analyst

● Led the customer insights and marketing research & analytics practice for Byju’s trial product experience programs

● Utilized advanced analytics and quantitative research methodologies to analyse data from social media data sources, survey data, customer direct feedback, customer service data, etc

● Built a logistic regression model in Python for targeted marketing campaigns that helped in reducing our churn rate by 14%

● Provided actionable recommendations from data mining, customer segmentation using k-means clustering, Market Basket Analysis with the goals of: optimising pricing, targeted marketing, cross-sell, up-sell, and retention opportunities

● Used Qualtrics to analyse the survey responses from focus groups to gauge the customers’ willingness to pay for various product pricing listing

● Implemented a model in Python to predict our potential customers for Email marketing campaigns leading to 20% reduction in marketing costs, increased inbound sales by 12%

● Coordinated with Web development team to perform number of A/B testing on our site which increased conversion rate by 7%

● Built dashboards in Power BI to analyse various KPIs of data and observe seasonality and cyclicity of our sales PROJECTS Develop Centralised Retail Data Warehouse (Power BI, Tableau, Power BI, Talend, SQL) September 2019 – December 2019

● Sourced and consolidated data from SQL Server, Oracle, MySQL, PostgreSQL databases and flat, XML and CSV to construct a star schema dimensional data model from various Systems Of Record (SORs) using Talend Studio

● Constructed Slowly Changing Dimensions (SCDs) to handle changing customer’s information

● Optimised different bulk ETL jobs for ensuring one click load of entire data warehouse using master job in 20 minutes

● Built interactive custom dashboards to analyse customer base, optimize inventory and maximize sales revenue using SQL, Tableau and Power BI

Customer Financial Database (SQL Server, ER Studio) January 2019 - May 2019

● Analysed enterprise requirements to design 3 NF normalised E-R model using ER Studio

● Designed and implement SQL procedures, tables, triggers, views and other database object for Enterprise Data repository

● Maintained SQL scripts, indexes and complex queries for extraction, analysis and reporting customer insights Web-Scraping and Twitter Sentiment Analysis [Python] January 2019 - April 2019

● Designed a Python web scraper for extracting tweets using Twitter API calls based on time, location and keywords supplied

● Schematized, refined and transformed raw twitter data using Python

● Recommended growth strategies based on the sentiment analysis of the tweets extracted of the products Portfolio Management and Algorithmic Trading of Tesla Stock prices [Python, Alteryx] September 2019 – December 2019

● Analysed patterns in stock prices via indicators such as MA, MACD, Bollinger bands etc

● Forecasted stock prices using PCA feature selection and ARIMA, multivariate linear regression with Fama-French factors

● Recommended trading strategies using prices and market oscillators such as OHLC distributions, ADS index

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