SUMMARY

• Proficient in executing data driven solutions to increase efficiency, accuracy and utility of internal data processing.
• Experienced at creating data regression models using predictive data modelling and analyzing data mining algorithms to deliver insights and implement action oriented solutions to complex business problems.
• Good experience in working wif Regularization techniques such as Ridge, Lasso and Elastic net and Dimensionality Reduction.
• Strong experience in R - programming and hands on experience on Python and its libraries for Data Science.
• Good experience in performing Data Collection, EDA, Data Engineering (Data Manipulation/Cleansing), Error Analysis, Fine Tuning a Model, Deployment, Validation, and Visualization.
• Hands on experience in deployment activities in cloud (Azure and AWS) for Production Environment and support.
• Knowledge on Big data technologies such as Hadoop, Spark.
• Experience wif successfully managing both internal and external stakeholders, delivering against projects, tasks and activities in a dynamic deadline driven environment.
• Proficient in articulating insights to both technical/non-technical and to senior business leaders.
• Exploring Text Analytics, Recommender Systems and Deep Learning.
• International Travel Experience: Central Bank of Philippines (Bangko Sentral ng Pilipinas), Manila (Submitted a POC).
• Experience in project management and agile delivery.
• Review the deliverables throughout development to ensure quality & traceability to requirements and adherence to all quality management plans and standards.

PROFESSIONAL EXPERIENCE

Sr. Data Scientist

Confidential

Responsibilities:

• Maintain and support data science models in production by working closely wif data science teams for improvements into existing models (Healthcare/Retail analytics/Banking/Customer Analytics).
• Predicting whether a patient TEMPhas diabetes or not, to provide a predictive understanding of individual buying behavior of consumers, predicting if someone who seeks a loan might be defaulter or non-defaulter, predicting if a customer is going to contribute Revenue generation (by purchasing) or not etc., are few of the projects I has worked on.
• Programming, debugging, testing, validation documentation and /ordeployment of package/ solution.
• Transform model outputs into formats dat are digestible to the end users through visualization, clear tables, and/or thoughtful presentations. Assist technical and non-technical end users in how to leverage and interpret the analysis.
• Experience working wif big data hosting/cloud computing technologies such as Azure.
• Worked on Shiny apps. Worked on converting SAS code into R packages.
• Worked on R- Package Performance improvement.
• Design, develop, test, deploy, maintain and improve ML models for the product/ application.
• Support for fixing R/ Python/ UI related issues, UAT, Production Deployment (to different environment and maintaining consistency for different Analytics Projects ) and Go Live .
• Instrumental in key integrations to ensure smooth build and configuration (CI/CD) using GIT.

Confidential

Data Science Consultant

Responsibilities:

• Responsible for developing and maintaining complex financial/ investment risk models such as Value at Risk (VaR: Historic, Parametric and Montecarlo), CVaR and stress testing models using R for equity, fixed income funds and Foreign exchange (FX) and deployed the same to various central & Non-Central Banks.
• Responsible for integrating R wif Java in onshore & offshore environments.
• Worked on segregation of stocks using K-means Clustering.
• Build, enhance and maintain Investment Portfolio Analytics such as portfolio optimization (Sharpe Ratio, Treynor Ratio & efficient frontier models).
• Assisted wif the development, implementation, and maintenance of the risk assessment process including data collection and validation.
• Applies predictive models to develop and execute appropriate segmentation and targeting for acquisition and portfolio strategies to provide insight into portfolio risk.
• Assisting wif gathering, validating, cleaning and organizing historical data for the bank's commercial portfolios.
• Document analytical methods, findings, conclusions, and recommendations for inclusion in reports to leadership.
• Worked on Performance optimization (improved from 48 mins to 3 mins) for one of the project NetOTC, for which got the client applauds.

Confidential

Big Data AnalystO

Responsibilities:

• Worked on data science project “Automation of loan eligibility” using machine learning algorithms in R.
• Having POC experience on SparkR, Tableau wif R & Time Series Analysis (Forecasting).
• Generating Reports using googleAnalyticsR, gggplot2 & Google Charts in R.
• Assisted while integrated R wif Hadoop (using the packages rmr2, rhdfs and implemented different DM techniques.
• Understanding the business requirement of analytical problem, performing exploratory data analysis to identify the sample dependencies and to identify the best model based on metrics.
• Testing of the final model using test data.
• Perform Ad-hoc statistical analysis (Descriptive Statistics) on data and produce actionable reports.
• Translate existing code from SAS into Python.
• Adept in using custom built packages for data cleaning, data preparation and execution.
• Manipulation of high volumes of data in terms of report generation.
• Work on data validation/development/documentation.
• Won various accolades pertaining to contribution to business like Platinum Service Award etc.
• Extensively trained new joiners and colleagues on process flow.