SUMMARY:

• Working as an Analytics Manager / Data Scientist from last 3+ years and having overall experience of 14 years.
• Prior to this I played the role of Lead Business data analyst for 6 years and Data Analyst for 3 years.
• I am having Sound knowledge of Math’s (Linear Algebra, Calculus, Graph, Probability and Statistics which helped me in performing Data Analyst / Data scientist role
• I am also having experience in Data Science, Big Data, Data Analytics and Project Management skills and diverse experience in Data analytics to help deliver transformational outcomes to insurers across the world.
• Have worked and supported clients in consulting environment for solution development phase to help them through analytics transformation and digital roadmap creation.
• Currently I am using Tableau 10.3 as Tableau is the richest data visualization tool.Apart from this I am making maximum use of Tableau ingenerating charts like Box Plot, Scatter chart, Pie chart and Histogram.
• Experience in Merchant Financing & Fraud Modeling, Supervised and Un - Supervised Learning Techniques, Time Series Analysis & Dash boarding reporting, Data Visualization and NLP
• Define project scope across Data Science, Data Analytics projects in collaboration with senior management and client.
• Create work plan and identify the activities or models which need to be completed or built during specific time period
• Determine Time, Effort, Resources and budget required to complete projects across streams.

TECHNICAL SKILLS:

• Big Data & Advanced Analytics
• ETL techniques
• Insurance Claims Analytics
• Banking Fraud Analytics (Banking)
• Project and Client Management
• Data Analytics
• Predictive Analysis
• Data Exploration
• R Statistical Software
• Python
• Machine Learning
• Hadoop and Map Reduce
• C
• C++
• Java (Initial Level)
• Cobol
• SQL, DB2, PIG, HIVE,SQOOP
• SAS
• Tableau

PROFESSIONAL EXPERIENCE:

Confidential, Phoenix

Data Scientist, Lead Business Data Analyst

Responsibilities:

• Helped Amex to Build the Decision System as a Data scientist by creating the Algorithm based on the business data, used Optimization Technique Simulated Annealing and Decision Tree ML concept. Statistical concept was widely used like Central Limit Theorem, Probability Concept, Probability Distribution (Binomial, Poisson and Exponential Distribution).
• Built Decision Trees in R to represent segmentation of data and identify key variables to be used in predictive modeling.
• Helped the Business in Amex Credit Card Analysis by building the Instant Decision Rules with the help of Hypothesis and Chi Square Testing in Merchant Finance Application.
• Manage project planning and deliverables for several projects across Advanced Analytics, Big Data and Digital Analytics streams.
• Project Consultant for Insurance clients across globe in their digital transformation journey.
• Resource hiring (Lateral & Campus), compensation fitting, training, coaching and performance review across different analytical streams.
• Managing Offshore and Onshore team workloads for project deliverables.
• Providing ad hoc support to senior management on project and technical
• Provide support in solution development for Data science, Advanced Analytics and Digital Analytics Projects.
• Guide team on data scientists, to develop statistical models and algorithms to answer complex business problems
• Implement machine learning techniques and interpret statistical results which are ready- consumption for senior management and clients.
• Provide Pre-sales support to Syntel team in RFP’s, RFI’s and Client presentations.
• Played a vital role with my analytics experience in Credit Risk Analysis where I helped the Business to take decision for loan funding to different sets of Merchant and generating the Reports in case of Decline with the help of Tableaureporting tool

Environnent: R, DecisionTree, Naïve Bayes Classification, Confusion Matrix, Tableau, R, Algorithm

Confidential

Scientist/ Analyst

Responsibilities:

• Extracting data from the database using ETL Concept.
• Excessive use of SQL and DB2 Query for Customer report
• Widely used the concept of Probability rules and Bayes theorem. logistic regression model in R

Environment: R, Handling missing values, Central Limit theorem, KNN

Confidential

Lead Business Data Analyst

Responsibilities:

• Played the role of Core Business Data Analyst / Scientist. Worked closely with Product Owner to understand the requirement.
• Build Decision Trees in Python to represent segmentation of data and identify key variables to be used in predictive modeling and also used Regression analysis, ANOVA and Z test.
• Modeling Helped Business and Engineering team to make decision based on data in Policy and Claims System by using Data Visualization technique to transform Unstructured data into structure format to do data visualization for business decision
• Worked on Insurance Healthcare Claim System.
• Use data to develop new statistical models to extract insights from large volume of data using the concept of Cluster Analysis, Neural Network, Random Forest and ARIMA modelling for identifying pattern in Time Series analysis.
• Developed and automated various modeling steps to make project-process faster and more accurate using Machine Learning Technique like Linear Regression, Non- Linear Regression, Logistic regression, Naïve Bayes Classification, Support Vector Machine, KNN etc.
• Create innovative algorithms behind variety of services ranging from Insurance Underwriting Risk to deciding right Insurance structure for all claims.
• Perform ad hoc statistical, data mining, and machine learning analysis, Develop and Design advance predictive analysis models using Python
• Performing Goodness of fit for various distribution and the best distribution which models the claims data
• Developed logistic regression model in R to predict whether policy is going to claim or not when hit by hailstorm (weather event):
• Extracting data from the database using ETL Concept.
• Validated the models on out-of-sample and out-of-time data.
• Spatial Analysis to determine possible relationships between claims and policies in force.

Environment: Python, Regression analysis, ANOVA and Z test, Linear Regression, Non- Linear Regression, Logistic regression, Naïve Bayes Classification, Support Vector Machine, KNN,ETL, Cluster Analysis, Neural Network, Random Forest and ARIMA modelling.

Confidential

Technical Lead

Responsibilities:

• Development and maintenance of code and data on base product.
• Involved in preparing the technical and business specification
• Experience of Handling Production support activities also.
• Report generation as per the specification provided by the client

Environment: Mainframes JCL, COBOL, DB2, VSAM, File aid, Alchemist, Excel, Easytrieve, Sort, Ice tool, Abend-aid, XPEDITOR, (Batch/CICS), CA7, JOBTRAC, Test Director 8.0, Quality Center