Rakshitha Nirmala

Motivated student pursuing Masters of Science in Business Analytics with over 3.5 years of experience in e-commerce industry. I am passionate about understanding the business problems and applying the knowledge to develop sustainable solutions for processes. Being a sports enthusiast, I have learned to look forward to success by taking the extra step required to be always ahead and successful. I am currently looking forward for internship opportunities to gain practical experience as Business Analyst Intern and improve my skillset to grow in my professional career.

• Performed deep dive analysis to auto-identify cases that can be excluded from audits to improve efficiency from 25% to 65% (09/2020 - 10/2020): As per the SOP followed by the team, the auditors are expected to identify the valid selections based on the listings uploaded by the customers. As part of which it was observed that in ~60% of audits, the auditors were not able to categorize the audit as either valid or invalid selections since there was missing information such as item name, Brand & UPC to perform the audit. Since investing time to check these cases does not help in improving the coverage for audits. To auto-identify such cases and exclude the same from audits, performed a deep dive on historic data to identify marketplaces, customers and Brands having most number of invalid cases and developed a logic to exclude such cases from audits before assigning the same to team. When we applied the logic for audits, the same helped the team in 1) filtering out/de-prioritize more than ~2.5K of SKUs on weekly basis, ~10K SKUs that are not required to be audited, saving ~15 FTE/Week. Further, this deep dive also helped in improving the efficiency of identifying the valid SKUs from 25% to 65%.
• Proposed an idea and built a Quick Sight dashboard to track team/business metrics (09/2020 - 12/2020): The team did not have a dashboard which populates key metrics and provide trends related to both business and operations. Took initiative of building the same using Quick sight, considering multiple variables such as marketplace, Brand name, Root cause etc. This helped in analyzing the data of more than 150K SKU's related to audits performed by the team, served as a single point of source for managers to find trends related to audits and helped in generating key insights.
• Developed an excel based solution to auto-populate root causes based on related drop downs (12/2020 - 12/2020): The team did not have a data validation in place for auto-populating relevant root causes sub root causes based on the decisions passed, which was leading to incorrect selection of sub root causes by the team. To fix this, developed an excel based solution to auto-populate root causes based on related drop downs and named ranges for each decision selected by the auditors. This ensured that only the dropdowns related to selected decisions will pop up in root cause column, avoiding errors related to decision marking by the team.
• Worked with team to create a macro for consolidating the data (09/2020 - 10/2020): The team was using macro to consolidate the daily audits uploaded by the auditors from the SharePoint. Which sometimes was throwing an error and was leading to misses while consolidation. To fix this, worked with my consolidation POCs from previous team to get the macro for consolidating all types of data from multiple sources when the format is same and implemented the same in current team. This helped us in 1) avoiding the errors and filling gaps while consolidating the data, 2) collating the required data for developing quick sight dashboard and performing analysis.
• Proposed ideas to improve the QA process by providing targeted feedback based on the reason code classification process (06/2018 - 09/2018): The team did not have a standardized mechanism to share targeted feedback to auditors. To fix this proposed a solution by applying DIMAC principals to create and share the QC data by creating 26 normalized reason codes, based on common errors (observed as per the historic data) identified by the QC auditor, that can be used to provide targeted feedback and help in reducing the errors. In addition, provided inputs to improve the existing audit tool to reduce the scope of manual errors.
• Performed a deep dive to determine world-wide acceptance, rejection rates and identify top contributing reasons (06/2018 - 09/2018): As part of the process followed by the team the auditors are required to perform a check on the images provided by the delivery associates and accordingly categorize the same by accepting/rejecting/escalating the case. Performed a deep dive on the historic data to identify WW acceptance and rejection rate for such cases along with the top contributing locations and reason codes using pareto framework to provide targeted feedback to the Delivery Associates (DAs).

• Spot Award (09/2020 - 12/2020) - Amazon - Received for creating an Quick sight dashboard.
• Above and beyond call of duty (06/2018 - 09/2018) - Amazon - Received for working beyond expected deliverables.
• Supernova Award (09/2017 - 12/2017) - Amazon - Received for contributing to operational goals set for the team.