OBJECTIVE:

To capitalize on my diverse analytical background in driving business solution and business growth through innovation and thought leadership.

PROFESSIONAL SUMMARY:

• Performance - Driven professional with 6+ years of experience across multiple domains such as Lifesciences & Healthcare, Banking & Insurance, Retail, Logistics, Communications & Workforce
• Experience in providing analytical solutions to real time business problems using Machine Learning/statistical algorithms that have impacted the business and user experience
• Well versed in solution areas as Customer & Geographical Segmentation, Text analytics, ROI analysis, Multi-Channel Optimization, Credit risk modeling, Market basket analysis, Key Drivers and Root Cause Analysis, Forecasting, Claims denial, Fraud Intelligence, Recruitment and Attrition analytics
• Substantial experience with techniques like Clustering, Regression, Time Series Forecasting, Text mining, Sentiment Analysis, Machine Learning Techniques like Neural Networks, Random Forest and Support Vector Machines, Bayesian network
• Knowledge of Hadoop Ecosystem and Big Data tools
• Ability to initiate and drive projects from conception to completion with minimal guidance

TECHNICAL SKILLS:

Statistical Tools: Python,R,SAS,H20,BigML

Techniques: Regression, Clustering, Decision trees, Association rules, NLP, Time series Forecasting, Neural Network, CNN,SVM, KNN, Random Forest

Big Data Tools: Pig, Hive, Apache Spark

Visualization Tools: Rshiny, Matplotlib,Tableau, Powerpoint, Excel

EMPLOYMENT HISTORY:

Confidential

Clinical Statistical Programmer

Responsibilities:

• Creation of intelligent benchmarks for claims KPIs using machine learning to reduce the noise in the existing alert framework
• Combination of time series methodologies to ensure more flexible model and to forecast the future values of KPI with tolerance limits based on the historical performance
• Automated Python script connected with Oracle database capable of running the analysis and generating the weekly results in the production environment

Confidential

Responsibilities:

• Intelligent matching of truck delay data & work order data
• Root cause analysis using text mining of work order description to find reason behind machine breakdown and the failed part(s) involved
• Sequence mining to identify pattern of machine breakdown

Confidential

Responsibilities:

• Creation of actionable segments by considering market and physician characteristics and in turn make promotional resource planning more effective
• Key Drivers Analysis to streamline parameters, Statistical regression analysis to find key drivers and selecting key drivers for cluster analysis
• Clustering & Segmentation by creating actionable customer segments based on the key drivers and developing insights around the physician segments

Confidential

Responsibilities:

• Creation of global routines that can perform calculations across clinical data and generates metrics to track the status of critical processes
• Key Risk Indicators (KRIs) at site and patient level to identify any discrepancies during trial process using automated scripts based on domain knowledge and advanced data science algorithms
• Systematically measure, target, prioritize and optimize promotion spends of the Gastro Intestinal drug across multiple channels, products and geographical territories
• ROI estimation for various promotion channels using Time Series Regression modelling and Test & Control methodology
• Brand and Portfolio level Marketing Mix Optimization (MCO) using Greedy Algorithm to suggest optimal budget allocation and forecast revenue across promotion channels and portfolio

Confidential

Responsibilities:

• Using Time series analysis to forecast the profit margin of company on monthly as well as daily basis at different levels
• Historical data analysis using box plots, trend charts to understand the different attributes that determine an associate at a rating level across multiple business units.
• Developed rules using decision trees for classifying different levels of performance rating and validated to check the accuracy of the classification.
• Identification of Outliers (incorrectly rated associates) and the attrition rate among these outliers. Further, using this information for the current appraisal process.

Confidential

Responsibilities:

• Developed statistical model using logistic regression to predict employee attrition
• Recommended & created new segments in the model that improved the efficiency of early warning system on attrition

Confidential

Responsibilities:

• Developed a logistic regression model to create optimal hiring profiles to streamline the current recruitment process using pre-hiring and Post-hiring factors of a new recruit
• Analyzing clinical study report, case report forms, study protocol to understand the objectives and requirements.
• Extensively using Base SAS and Macros to clean, map and transform raw Data into Client's standard or client data into CDISC SDTM standard.
• Prepared data for FDA submission as per CDISC submission standards and guidelines; Created annotated case report forms (CRF) using CDISC-SDTM mapping.
• Generated table, listing & figures (TLFs) from the target datasets and validated data sets using the legacy reports.