Senior Data Scientist/ Analyst Resume

SUMMARY:

Data Science and Analytics professional with 7 years of experience in Data Analytics, Machine
Learning based Model Development, Business Intelligence, Data Engineering and Product Development
Expertise and knowledge of design & execution of complex analytical solutions by analyzing business problems, generating business insights using data, developing predictive models, interpreting results and recommending strategies
Extensive experience in Data Mining using SQL to deep dive into structured, semi - structured & unstructured datasets for actionable business insights
Expertise in Data Visualization, Metrics Reporting & Storytelling using Tableau
Proficient in SAS, R and Python for data wrangling, data analysis, model development and deployment

TECHNICAL SKILLS:

Business Intelligence / Big Data / Data Science: SQL, Hadoop (Hive/Impala), R, Python and SAS

ETL / RDBMS: Teradata, SQL Server, Oracle, Alteryx,

Data Visualization / Reporting: Tableau, Looker, Spotfire, RShiny, Excel (Advanced)

Version Control & Agile / Others: Git, BitBucket, JIRA

Machine Learning / Statistical Modeling: Linear Regression, Logistic Regression, Decision Trees, Random Forests, Gradient Boosting Machines, SVM, Cluster Analysis, Text Mining

Specialties: Data Analytics, Product Analytics, Growth Strategy, Business Development and Insights

PROFESSIONAL EXPERIENCE:

Senior Data Scientist/ Analyst

Confidential

Developed and managed “Compensation Benchmarks”, a data product from inception to sales, that resulted in $1.5M monthly subscription revenue using data science and analytics that helps employers to allocate hiring budgets
Analyzed customer data and segmented customers to drive revenue and growth by Identifying potential customers for cross selling products
Built and implemented predictive models for income prediction that resulted in improving fulfillment rates by 10% across multiple verticals for Verification Services business unit
Analyzed product data by developing dashboards and reports using Tableau, by creating KPIs and metrics to drive revenue and to seize new business opportunities

Senior Data Scientist

Prudential Financial

Generated $20M additional assets under management by developing new components and capabilities to retirement products through the use of data analytics
Developed predictive models using SAS & R, to predict the retirement probability for better retirement plan design and to quantify the cost of aging workforce, ‘First of its kind’ in Prudential’s US retirement business
Developed Predictive models using SAS & R, to predict the duration of claims to develop business strategies that accounts for premium pricing, to help business in estimating the overall cost and to adjust current claims management process
Pioneered and assisted data technology team in creating a new claims data management system that increased transparency, accountability and performance accuracy
Developed dashboards and visualizations using tableau, to showcase KPIs, trends / patterns on the historical data and to present Business Units

Senior Data Analyst/ Scientist

Confidential

Worked on customer segmentation and marketing campaign strategies for profiling, targeting and acquisition of customers and categories to optimize client's investments and to achieve better ROIs
Worked on Uplift modeling for marketing campaign optimization to identify customers with greatest response lift from the campaign
Built predictive models to classify customers, to predict customer churn rate, to predict customer spend etc., using methods like K-means clustering, random forests and linear/logistic regression
Performed data cleansing and data manipulation for data preparation, performed ad hoc reporting and exploratory data analysis to identify key drivers, patterns, analyze and interpret data
Master of Science, Business Analytics
Bachelor of Technology, Information Technology
Individual Life Insurance Lapses
Explored insurance company data and developed predictive models to predict customer lapses
Evaluated models against business and statistical metrics to derive the main failure contributors
Performed secondary research to gain insight on the importance of early identification of a customer who is likely to lapse and the business value it holds
Written white papers on Support Vector Machines using R and Fundamentals of Apache Spark
Priceline Data Challenge: Developed segments to identify different customer groups based on their buying pattern
Built predictive models using various classification and regression methods to find out Customer churn rate prediction and Customer segmentation in Insurance, Retail & E-Commerce domains using SAS and R

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