Abhijith Asok

Safecity is a non-profit based out of Mumbai, India which crowd maps data on gender-based harassment and uses innovative Data Analytics to combat it. With active partners in multiple countries such as Kenya, Cameroon and Nepal to collect data on a global scale and collaborations with Twitter and UPenn, Safecity works closely with the Police forces of multiple cities to enable it's unique data and analytics to create a difference in a dire cause. Following my brief tenure as a volunteer for data analysis, I took over as the Head Data Scientist to build a data science team for Safecity and continually brainstorm and direct the new data team into newer angles of analysis to extract deeply hidden insights. The role also enables direct collaboration with multiple US-based Data Organizations. My work includes(but isn't limited to): Creating and maintaining elaborate dashboards(using MS Excel and Tableau) to dynamically update time- based(multi-level) and/or harassment type-based and/or location-based incident trends to continually gather updated insights Implementing Text Analysis using R to dissect textual descriptions of incident reports to gather indirect insights about incident nature, type of location, situational correlations etc. Drawing parallels from Retail's Market Basket Analysis to analyze the relationship between different types of harassment crimes Clustering the incident reports based on inclusive incident types using segmentation techniques like Latent Class Analysis using R/SAS Developing a multi-code system using Voronoi Diagrams and clustering techniques like K-Means(Using SAS) to effectively identify and easily point to any locality under Safecity's consideration, with space for dynamic additions moving forward Keeping a track of various social media trends about gender-based crimes, both within the organization and externally, using methods such as Twitter analytics, using R

Created a dynamic network intelligence data visualisation dashboard on Tableau aimed at network optimization and a long-term organizational saving of $7m

Worked with the COO to optimize construction material purchases through price tracking, time series forecasting and report automation.