Data Scientist, Drop
Drop is a loyalty platform that combines e-commerce and financial technology to provide their customers with rewards on everyday purchases.
I drove impactful initiatives across various domains, collaborating closely with teams to conduct in-depth analyses and propose actionable recommendations. This led to significant improvements, including a 2X increase in user retention and offer completion metrics in onboarding experiences, as well as monthly savings exceeding $10,000 through proactive fraud detection measures. Using predictive analysis, I guided strategic product development decisions, aligned with user preferences. Additionally, I facilitated data driven decision making by creating dashboards for product initiatives, empowering stakeholders with actionable insights.
Improvements in Onboarding Experience: Collaborating closely with the product team, I conducted a comprehensive analysis of users' onboarding experience. This involved diving into user data during the onboarding flows and conducting a statistically significant analysis to uncover key insights. Based on these findings, I proposed actionable recommendations aimed at enhancing the onboarding process. Through collaborative discussions with stakeholders, including the product team, these recommendations were prioritized and implemented. As a result of these efforts, we witnessed a 2X increase in both user retention and offer completion metrics, signaling a significant improvement in the overall onboarding experience.
Fraud Detection and Savings: Upon identifying anomalies in financial transactions, I collaborated closely with the finance department to investigate further. Through further analysis, we uncovered a significant number of user accounts engaged in fraudulent in-app activity. To address this issue, I spearheaded the development of tailored fraud detection mechanisms, including the implementation of preventive fraud alerts. These measures not only resulted in the proactive detection and mitigation of fraudulent activity but also led to substantial monthly savings exceeding $10,000.
Predictive Analysis for Product Development: I analyzed user behavior and attributes to better understand our user base. Using this information, I conducted predictive analysis to forecast feature adoption rates among different user groups. The insights gained from this analysis were instrumental in guiding strategic decisions for product development. By aligning our product roadmap with the preferences and behaviors of our user segments, we were able to create a more customized and effective product offering.
Dashboard Creation for Product Teams: In addition to my involvement in data analysis projects, I played a key role in creating dashboards for various product initiatives. These dashboards were instrumental in providing stakeholders with actionable insights and facilitating data driven decision making across different teams within the organization.
