SUMMARY:

• Quantitative model Development and Validation with 7+ years of total Finance, IT and E - commerce experience working on Data Science Projects, which involves Data analysis, Quantitative model development &Validation and implementation.
• Extensive experience in Quantitative model development and validation for Prepayment and funding models, Stress test models, Market risk Models, Credit risk Models
• Monitoring, measuring and reporting of customer matrices. Building advanced segmentation models to increase customer frequency as well as customer acquisition strategy.
• Professional experience in predictive modeling, logistic regression models, linear regression models, Time series analysis, cluster analysis.
• Professional to maintain model development and validation technical documentation, presentation,
• Excellent communication and interactions with cross-functional teams globally.

TECHNICAL SKILLS:

Technology and Tools: Data Science, Quantitative Data Modeling & Validation Machine Learning, Big Data, R, SAS, Python, SQL, NoSQL, Hadoop, Hive, Pig, Spark, SSAS, SSRS, Tableau, Qlik View Data Science Quantitative Model Development Quantitative model Validation Machine Learning Predictive modeling Advanced Data & analytics Big Data analyst Technical Documentation SAS(certified professional) SAS enterprise Miner SAS enterprise Guide R python SQL MySQL NoSQL Hadoop Hive Pig Sqoop Spark Scala Hbase SSRS, SSAS, SSIS Tableau, Qlik View Linux, UNIX C SAP ERP Gephi

PROFESSIONAL EXPERIENCE:

Data Scientist (Quantitative Lead Analyst)

Confidential - Norwalk, CT

Responsibilities:

• Worked in prepayment (PD) and funding models with deep understanding of about logistic regressions, multi-linear regressions, hypothesis testing, goodness of fit and back testing.
• Worked as independent validation and peer review of all newly-developed complex predictive models to ensure they follow good modeling practices and are in compliance with Model Governance Policy requirements.
• Built Alternative models and challenge the models.
• Analyzed distributions of variables against credit card declination indicator and customer defection indicator.
• Evaluated the criteria for profitable customers through Hypothesis testing, Linear Regression and Cross Tabs.
• Logistic Regression model was built to predict the customer retention rate using SAS EM.
• Worked in prepayment (PD) and funding models with deep understanding of about Logistic regression, multi-linear regressions, hypothesis testing, goodness of fit and back testing.
• Worked as independent validation and peer review of all newly-developed complex predictive models to ensure they follow good modeling practices and are in compliance with Model Governance Policy requirements.
• Worked in prepayment (PD) with deep understanding of about Logistic regression, Markovian chain s, hypothesis testing, goodness of fit and back testing.
• Worked as independent validation and peer review of all newly-developed complex predictive models to ensure they follow good modeling practices and are in compliance with Model Governance Policy requirements.
• Built Alternative models and challenge the models.
• Worked in prepayment and funding, Stress test models(VAR), Market Risk, Credit Risk(PD, LGD, EAD) with deep understanding of statistical and Machine Learning concepts about logistic regressions, multi-linear regressions, K-means Clustering, Ridge, Lasso, SVM, Neural networks, goodness of fit and back testing utilizing R, SAS, SQL.
• Professional to maintain model and model validation documentation including model prototypes, model development documents, and implementation documents and supporting material, model testing, research documents, and documents related to external model reviews and regulatory examinations.
• Predictive modeling, time series, Cluster analysis, Regression Analysis, hypothesis testing, T-test, ANOVA, and Forecasting, visualization tools (Tableau, QlikView) to build effective reporting and analytics.

Sr. Business/Data Analyst-Big Data, Statistical, Predictive Modeling

Confidential

Responsibilities:

• Was accountable for day to day co-ordination with team members, collecting data, data analysis, Predictive Modeling, build and support data pipelines, ad hoc data analysis and making necessary action plans to deliver product in time.
• Experienced with data mining and distributed computing (Hive/Hadoop) by using SAS, SQL, OLAP, R, complex SQL, Stata, Tableau, and ETL knowledge on data warehousing.
• Monitoring, measuring and reporting of customer metrics. Support the building of advanced segmentation models to increase customer frequency and spend, as well as drive customer acquisition strategy
• Good understanding of statistical and quantitative data analysis such as linear models, multivariate analysis, Predictive modeling and time series, Categorical data and Regression Analysis, hypothesis testing, T-test, ANOVA, and Forecasting.
• Evaluating product present cost, and discounted cash flow analysis to find out project cost by using the Time value of money. Generating financial reports to find out either incoming and outgoing cash flows, net present value (NPV), KPI, CLV, income or P&L impact on product.
• Worked cross-functionally on the project with the hardware design, validation and software development teams and incorporating the Statistical and Analytical techniques in engineering practices and driving formal Design of Experiment.
• Have excellent demonstration of written and verbal communication skills, high energy strong mathematical, Excel skills.