Apr 2020 – Mar 2021 · 11 mos · Bengaluru, Karnataka, India

Jun 2019 – Mar 2020 · 9 mos

• BUSINESS ANALYST
• Designed and implemented RFM marketing strategies from scratch to achieve 15% MOM growth from November to January through better retention and reactivation.
• Finalised offers and marketing strategies to increase rides with low burn basis learning from different campaigns.
• Initiated station level PnL Process to close high burn stations to save 5-10 Lakhs parking cost each month
• Designed a process to open 100+ new stations PAN India by using K-means Clustering from OLA demand insights and using demographics.

Jun 2019 – Mar 2020 · 9 mos

• OPERATIONS ANALYST
• Collaborated with product and operations team to implement process and provide data insights for better asset health management.
• Worked with product team and defined the customer feedback inputs of vehicles for maintenance (i.e. type of damages by showing the parts of vehicle through image) after ending and cancellation of rides by customers.
• Set up the process of Hub Station Servicing after doing cost benefit analysis of Service Centre Servicing vs Hub Station Servicing and brought down the logistics cost per vehicle by 65% from Rs.200 to Rs.70
• Worked with product team to built module for figuring out the vehicles where we need to take proactive measure of air filling activity for tyres by providing insights and business/cost benefit analysis to reduce daily average maintenance inflow by 1% (140 vehicles), improvement in tyre life and help in building better customer experience.
• Set up the process of Spare Part Reconciliation at warehouse from mechanics end with the use of current product which helped in increase of reconciliation from 6% to around 85%.
• Defined metrics for Maintenance i.e. Productivity of Mechanics, Average resolution time per kind of disposition resolved, Recurring of vehicles etc. which are useful in tracking of Under Maintenance Vehicles/ Instances.
• Worked on Mechanics optimisation by doing analysis of their Time and Motion Study which helped in defining the mechanics daily average productivity on which they can be evaluated and tracked.

Jul 2018 – Jun 2019 · 11 mos · Bengaluru Area, India

• Project - Predictive Maintenance of Coal Boilers(POC)
• Predicted Thickness of pipes at every outage of every unit boiler using Gradient Boosting in Classification and Regression Tree modelling with an accuracy of 72% and Recall of 84%.
• Predicted Risky/Not Risky status of pipes at every outage of every unit boiler’s economizer and re-heater using Logistic Regression with Recall of 94% and 87% respectively.