Jithin Narayanan

About Jithin Narayanan

As a Data Scientist/Data Engineer at Intel Corporation for over 7 years, I have designed,…

Experience

1. Data Scientist

   Intel

   Jul 2018 — Present

   Designed, Developed and Parsed Epi Data (3-inch wafer) from Epi data source to Big Data Ecosystem (Hadoop). • Used R and Python programming to develop different scripts needed for LightSource project. • Created table and Views using SQL, IMPALA, HIVE and SPARK to fetch the data from LightSource and do analysis based on the requirements. Used GitLab for storing code repository. • Worked on logging for all the scripts I created, used Postgres for storing logs and PostgreSQL to access it. • Created different Shiny Apps for users to do data visualization. Shiny is a library in R which helps to develop different plots based on user definition. Also used Shiny to do different kind of data analysis. • Lead a team of 5 to do problem solving, parse data, build models, analyze data, do statistical modeling and techniques, quantitative analysis for Module Power Data Analysis project. • Used Cloudera manager to get the details of our remote systems and their health. • Used NLP, Deep Learning techniques for predicting the reviews given by intel external customers.Used Scikit learn, tensorflow,nlt packages in python for the above. • Created Traceability scripts between Epi Data (3-inch wafer), MLT Data-(Module Data 12-inch wafer), WLT Data (Wafer Level Test Data). • Worked on system admin side on installing different python and R packages needed for our projects because our REDHAT system is behind a firewall and used different sudo and other Linux commands based on project requirements. • Used ETL to extract high volume data and build different algorithm based on different projects. • Designed, Developed, tested, deployed and scheduled code (SDLC Model) for different Projects. • Used R and Python for Machine Learning models like Decision Tree analysis, Regression, Classification Analysis, SVM and Interactive Decision Analysis for predicting the result of different requirements from intel projects. • Used Large Language Models(LLM) using python LangChain library.

Skills

• Javascript
• Html
• Java
• Microsoft Office
• Sap
• C++
• Sql
• Abap
• Matlab
• Microsoft Word
• Programming
• Microsoft Excel
• Powerpoint
• Teamwork
• C
• Sap Abap