• Designed and implemented machine learning pipelines to clean raw sensor dataand generate predictor variable for model training• Analysed sensors data and aided in development of Machine learning model topredict fuel lanes space availability with an accuracy of 97%• Performed statistical data analysis from multiple magnetometer sensors toidentify patterns of shadow effects generated from passing trucks• Prepared and presented weekly reports on data quality assurance• Automated data pipelines for storing and monitoring real time data fromparking sensors• Utilised parking data from sensor deployments to generate usage and parkingviolations insights using pandas• Designed and led development of Fybr Insights backend in python andimproved performance by 30%• Aided in development of real time parking violations calculator using NodeJs

• Developed a Random Forest-based model to continuously analyzemagnetometer values and predict whether an L Category Vehicle is present in alocation in a parking lot.• Performed Statistical analysis to identify optimum position of parking sensorlocation in a parking space