$14.0 per Hour
A performance driven ambitious Data Architect with an extraordinary blend of and technical knowledge. Ability to communicate and motivate team members to enhance strategic goals and bottline objectives. Creative problem solving and troubleshooting skills complemented by meticulous attention to details that will result in the success of an organization by developing new applications or improving existing ones.
Tech Stack Expertise
Microsoft SQL Server
AWS S3,AWS2 Years
MS Azure0 Years
- January 2013 - January 2023 - 10 Year
Logistics For Clothing
- January 2014 - January 2015 - 13 Months
Analyse and predict the returns for particular brand/ region/ Colour/ Material/ Cost etc.
for the clothing. Developed the Data Architecture and model deployment plan for the client.
Taxation Fraud Analytics
- January 2015 - May 2017 - 29 Months
To identify tax evasion for medium and large-scale traders for the goods movement:
Process and analyse the data using SQL to understand the reasons for cancellation, rejection, and unknown trader details.
Perform clustering/ segmentation and Market basket analysis to analyse the pattern for movement of goods.
Developed a Tableau/Power BI dashboard for ministries to analyse the observed anomalies and identify the traders
Engineering Workspace Analysis
- May 2019 - August 2019 - 4 Months
Worked on a couple of dashboards:
Engineering dashboards to track the job runs and highlight the errors to test the data quality.
Compare 2 cloud workspace attributes to help with cloud migration projects.
E2 Migration PoC
- June 2019 - December 2019 - 7 Months
Migrate the data for the client from one workspace to the databricks workspace.
Build an ELT process and Data pipeline – Develop the same on databricks workspace.
GLM Pricing Models
- January 2018 - January 2020 - 25 Months
Develop Risk Premium Models for Motor & Property using EMBLEM, RADAR & Python.
Risk Premium for Retail Motor – Model the frequency, severity and propensity for different perils to develop a risk premium model.
Model Validation and Backtesting – Monitor the model and simulate the data to improve the performance of the model in the market.
Gradient descent boosting method – Use GBM to optimize the residual for the model to achieve a better accuracy.
in PGP in Machine Learning & Artificial IntelligenceDelhi Institute
- November 2018 - December 2019