Ramesh Rajan Email and Phone Number

Acitvely invovled in research, data analytics, leading and developing machine learning based models for account management, consumer/credit risk scoring and originations.

1. Delivered production models by using descriptive analytics on wide-variety of large transaction datasets. 2. Day-to-day activities involved data inference, data processing and feature engineering in Python, PySpark, Java, SQL and Kotlin

Worked on unique product development solution pertaining to risk and compliance system.

1. Large scale data analysis of Healthcare and Pharmaceutical data using KDB with AWS(~2B rows of data, equivalent to 5TB). 2. Developed interactive visualization dashboards for micro-applications based on healthcare and pharmaceutical data using D3.js, Javascript.3. Implemented machine learning algorithms: knn, k-modes, SVMs, Association Rule Mining, Gradient Boosting Machines etc. for Adverse Events Analysis(FDA FAERS dataset) by integrating R, and Python. Achieved an accuracy of about 89% with ARM, and GBMs.4. Further reduced the dimensions of the datasets using PCA, and identified the features contributing the most to the variance.5. Executed complex queries in qSQL for kdb+ for fetching the information efficiently, and cleaning the data.

1. Analyzing past and current student enrollment data to identify the popularity of the program and outreach. And also suggest any improvements/changes needed in the program.2. Maintenance of Data Science Initiative website using HTML5.3. Developing visualizations for better understanding of the demographics using Tableau, R, and Python.

1. Worked primarily on developing prototypes. Been a part of the research and development at Atos.2. Built an interface application to easily import and analyse data for SIEMENS grid analysis software and GIS.3. Modeled the data for analysis purpose by using ETL/ELT processes: Geocoding the data, dimensional space transformation etc.. Expanded the framework of the application to handle multiple transformation requests for ESRI ArcGIS, Google Maps, and SCADA.4. Built models for several devices incorporated in the network with various combinations. 5. Incorporated machine learning algorithms(Random Forests, Regression models) for identifying the source and timing of impacts in the system. System expansion and planning were a part of the model.6. Tested and optimized models in MATLAB, Java, and Python. 7. Actively contributed to reducing man hours in the projects and generated revenue by increasing the client base.

Implemented IBM Infosphere STREAMS(an advanced analytics platform) for grid analysis and provided insights from the results w.r.t electrical grid behavior in different scenarios. Results yielded an accuracy of 94.3%.