Work Experience

DELOITTE CONSULTING LLP

MARCH 2022 - PRESENT

       Data Scientist – Toyota Motor North America

• Utilized ML to optimize low forecast accuracy which leads to inefficient inventories and PPO stripping.

• Built univariate time series model (ARIMA, SARIMA, TBATS, Prophet etc..) considering the historical installation rate

  and forecasting at VDC level (2 VDCS, Princeton and San Antonio).

• Built multivariate time series model (Ridge and Lasso regression) considering installation rate, TLS order history, vehicle

  production history, vehicle forecast ETA, and weather data.

• Achieved target POC within 3 sprints and improved forecast accuracy by 14% - 40%.                                                  Data Scientist – Insights Cloud

• Automated/accelerated the data integration pipeline using AWS S3, glue, sagemaker, and knowledge graphs.

• Data integration was automated by using AWS glue data catalog and raw data was sent into the landing zone/data lake (AWS S3 bucket).

• Used AWS glue crawler to do preliminary data discovery and executed native ETL using AWS glue in python.

• Explored knowledge graphs using Keras BERT in AWS sagemaker to find corresponding entities across all data sources.

• Data visualization was done using Tableau.

• Explored Semoss as an alternative to knowledge graphs to find insights between multiple data sources.  

SMART EMBEDDED SYSTEMS HARDWARE AND SOFTWARE DEVELOPMENT INTERN, GLOBAL QUALITY CORP (GQC)

JANUARY 2021 - APRIL 2021

• Leveraged electrical engineering and artificial intelligence background in order to improve key product development projects including Smart IoT hardware and software, and pipeline failure prediction software.
• Identified predictors of pipeline failure by using various machine learning architectures (ANN, LSTM, GAN, CNN, regression models).
• Created and modified/debugged code using Python, R, C++ to customize pipeline failure scenarios.
• Learned GO and Implemented microservices that get humidity from sensors connected to Raspberry PI.
• Performed Code Review and unit testing for the pipeline data set with project team.
• Mentored fellow team member concerning implementation of machine learning algorithms.
• Communicated remotely with managers (California) to coordinate and assign work via Microsoft Teams.

CONNECTED & AUTOMATED VEHICLES INTERN, HONDA R&D AMERICAS, INC.

MAY 2019 - AUGUST 2019

• Collaborated with partners and suppliers to implement, test, and validate CAV applications (lane speed monitoring (LSM), left turn assist (LTA), pedestrian warnings, traffic light indicators, etc.)
• Conducted both in-lab and in-vehicle test and data analysis.
• Coordinated with the Automotive Technology Research (ATR) Group via Agile Methodologies using JIRA.
• Automated the initial setup of the V2X module using Python (established SSH connection to securely transfer files) and the CAN reflection tests after installation of the module using LABVIEW.
• Evaluated various wireless communication technologies and/or techniques such as Dedicated Short-Range Communication (DSRC) and/or cellular communication (5G) for testing compatibility and efficiency of V2X module.
• Built wire harnesses for connected vehicles to obtain specific data from the CANBus (CAN-1) outlet. Data collected included speed, gear selection, headlights, acceleration, etc.

RESEARCH INTERN AT INTEGRATIVE BIOSENSING LABORATORY

MAY 2016 - AUGUST 2018

• • Our lab is utilizing nanopore sensing and micro fabrication techniques to develop handheld and wearable diagnostic devices.
  • Programmed new equipment (power supplies, oscilloscopes, etc.) using LabVIEW.
  • Created circuits for particle tracking and conducted experiments for particle tracking using tracker.
  • Used MATLAB for modelling the spikes during the particle tracking.
  • Used SolidWorks to create a model (the nanopore) of the experiment and its workings.
  • Assisted with the editing of manuscript about particle tracking.