Candidates About

Sandhya

K L College of Engineering                                                                                                                                                Guntur, India                                                                                                                                         B.S. Computer Science                                                                                                                                                     Aug 2006 – May 2010

TECHNICAL EXPERTISE

WORK EXPERIENCE

SUMMARY

·         7+ years of experience in Data Science with expertise in Descriptive, Inquisitive, Predictive and Prescriptive analytics.

·         Experience working with senior stake holders to understand the business requirements and present actionable data insights to Senior management.

• Experience working with large data sources (5Bn rows+); interpret and communicate insights and findings from analysis and experiments to both technical and non-technical audiences.

·         Strong computational background (complimented by Statistics/Math/Algorithmic expertise), healthy portfolio of projects dealing with Big Data, solid understanding of machine learning algorithms, and with a love for finding meaning in multiple imperfect, mixed, varied, and inconsistent data sets.

PHOTON INFOTECH                                                                                                                             Bohemia, NY

Data Scientist                                                                                                                                                                      Sep 2017 – Present

Developing a personalized recommender engine for the client Natures Bounty Co. The products are recommended to the user based on the user preferences (questionnaire taken by the user when entered the website). The responsibilities involved are exploratory data analysis, modeling of the data. Used Excel for some part of the Data manipulation. Affinity score matrix is built for the products and their corresponding questionnaire. Used classification algorithms such as KNN (K-Nearest Neighbours) and Random Forests algorithms. Build these models in python. This enables user to engage better.

APPLE INC                                                                                                                                               Cupertino, CA                                                                                                                                                   Data Scientist                                                                                                                                                                   Jan 2015 – Aug 2017

Played a key role in developing and maintaining statistical and machine learning models that mine, analyze and turn Apple data into insights that is helping Apple grow their user base and revenue.

KEY PROJECTS

Purchase Propensity Modeling

·         Developed classification machine learning models in python that predicted purchase propensity of customers based on customer attributes such as demographics – education, income, age, geography, historic purchases and other related attributes. Predicting customer propensity helped marketing teams to aggressively pursue prospective customers.

Customer Churn

·         Developed classification models to predict the likelihood of customer churn based on customer attributes like customer size, revenue, type of industry, competitor products and growth rates etc. The models deployed in production environment helped detect churn in advance and aided sales/marketing teams plan for various retention strategies like price discounts, custom licensing plans etc.

Customer Life Time Analysis

·         Projected customer lifetime values based on historic customer usage and churn rates using survival models. Understanding customer lifetime values helped business to establish strategies to selectively attract customers who tend to be more profitable for Apple. It also helped business to establish appropriate marketing strategies based on customer values.

Customer Segmentation

·         Developed 11 customer segments using unsupervised learning techniques like KMeans and Gaussian mixture models. The clusters helped business simplify complex patterns to manageable set of 11 patterns that helped set strategic and tactical objectives pertaining to customer retention, acquisition, spend and loyalty.

Forecast Process Innovations

·         Improved sales/demand forecast accuracy by 20-25% by implementing advanced forecasting algorithms that were effective in detecting seasonality and trends in the patterns in addition to incorporating exogenous covariates. Increased accuracy helped business plan better with respect to budgeting and sales and operations planning.

Cross Sell and Upsell Opportunities

·         Implemented market basket algorithms from transactional data, which helped identify products ordered together frequently. Discovering frequent product sets helped unearth Cross sell and Upselling opportunities and led to better pricing, bundling and promotion strategies for sales and marketing teams.

RETAILMENOT INC                                                                                                                                       Austin, TX

Data Scientist                                                                                                                                       Dec 2013 - Dec 2014

KEY PROJECTS

Offer Recommender System

·         Developed a personalized coupon recommender system using recommender algorithms (collaborative filtering, low rank matrix factorization) in python that recommended best offers to a user based on similar user profiles. The recommendations enabled users to engage better and helped improving the overall user retention rates.

User Click Prediction

·         Developed a lead scoring system by modeling the users based on company size, industry segment, job title or geographic location using supervised learning algorithms. Scoring leads led to increased sales efficiency and effectiveness, increased marketing effectiveness and tighter marketing and sales alignment.

Other Projects

·         Designed the Data Warehouse and MDM hub Conceptual, Logical and Physical data models

·         Used Normalization methods up to 3NF and De-normalization techniques for effective performance in OLTP and OLAP systems. Generated DDL scripts using Forward Engineering techniques to create objects and deploy them into the database.

BANK OF AMERICA                                                                                                                                                                 Charlotte, NC                                                                                                                                                   Data Scientist                                                                                                                                                                     Oct 2012 - Nov 2013

Played key role in developing and deploying Dodd-Frank Act Stress Test models across several bank portfolios. Provided architectural leadership on several high priority initiatives including account prioritization, account prospecting, and opportunity scoring. Drove the creation of comprehensive datasets encompassing user profiles and behaviors, and incorporating a wide variety of signals and data types.

KEY PROJECTS

Top down Models - Residential Real Estate

·         Automated the scraping and cleaning of data from various data sources in R and Python. Developed Banks’s loss forecasting process using relevant forecasting and regression algorithms in R.

·         The projected losses under stress conditions helped bank reserve enough funds per DFAST policies.

Credit Risk Scorecards

·         Developed several interactive dashboards in Tableau to visualize 8 billion rows (1.2 TB) credit data by designing a scalable data cube structure.

·         Built credit risk scorecards and marketing response models using SQL and SAS. Evangelized the complex technical analysis into easily digestible reports for top executives in the bank.

CUMMINS, INC                                                                                                                                                                        Pune, India

Data Modeler/Data Analyst                                                                                                                                  Jul 2011 - Sep 2012

·         Designed scalable processes to collect, manipulate, present, and analyze large datasets in a production ready environment, using Akamai's big data platform.

·         Achieved a broad spectrum of end results putting into action the ability to find, and interpret rich data sources, merge data sources together, ensure consistency of data-sets, create visualizations to aid in understanding data, build mathematical models using the data, present and communicate the data insights/findings to specialists and scientists in their team.

·         Implemented full lifecycle in Data Modeler/Data Analyst, Data warehouses and DataMart’s with Star Schemas, Snowflake Schemas, and SCD& Dimensional Modeling Erwin. Performed data mining on data using very complex SQL queries and discovered pattern and used extensive SQL for data profiling/analysis to provide guidance in building the data model.

HEWLETT-PACKARD                                                                                                                          Bangalore, India

Data Analyst /Data Modeler                                                                                                                               Jun 2010 - Jun 2011

·         Worked with SME's and other stakeholders to determine the requirements to identify Entities and Attributes to build Conceptual, Logical and Physical data Models. 

·         Used Star Schema methodologies in building and designing the logical data model into Dimensional Models extensively. Developed Star and Snowflake schemas based dimensional model to develop the data warehouse. Designed Context Flow Diagrams, Structure Chart and ER- diagrams.