• Lead a team of three analysts at a film investment firm, establish algorithms to predict box office performance of domestic and international films in China to support investment decisions• Collect film related data from numerous online sources using Python, including production, distribution, performance and reviews• Build the film related database using MySQL in Cloud 9, with over fifteen variables for films screened from 2015 to present• Visualize the data with Excel and Tableau, and prepare materials for limited partners communication • Conduct exploratory data analysis on film related data to determine the main characteristics and design the initial approach• Develop regression analysis and A-MPM modeling with STATA to forecast box office performance of specific films, and identify investment targets based on significant parameters in a variety of film categories

• Used path analysis through the multi-level model of engagement to predict the business-oriented performance for Super-Pharm (a pharmacy chain in Israel, Poland, and China)• Managed employee-level, department-level and branch-level data of Super-Pharm in Excel, cleaned and reshaped five years of raw financial and operational information on various levels• Conducted regression analysis based on the Bootstrap method, mediation and mediated moderation models, and constructed forecasting models in STATA to predict performance metrics such as productivity and turnover• Analyzed hundreds of pages of qualitative data (e.g., minutes of the meeting) with Python to find the key text in order to identify the major problems for TATA (an Indian conglomerate) with their induction training program, and provided suggestions for focus areas during the training(Guide: Prof. Peter Cappelli and Researcher Liat Eldor)