Wahyu Dwi Prasetio's Resume

Improved location grading accuracy for business planning by implementing cluster-based logic and compiling reference data—accelerating market assessments and reducing manual effort.

Identified high-growth property areas by analyzing historical listing trends, enabling data-driven asset canvassing and expansion decisions.

Streamlined investment reporting by integrating review, rating, and amenity data scraping, and developing an interactive EDA notebook—enhancing data quality and speeding up report generation by 1.5×.

Developed and iterated revenue strategies using experimental results and external market data, targeting to AUM growth and achieving semester-based business targets.

Designed and automated pricing execution based on dynamic time-segmented strategies, reducing manual workload and enabling real-time performance monitoring via a centralized dashboard.

Facilitated mentee growth by leading study groups, tracking progress, and supporting instructors

Batch 2023: Received 4.85/5.00 average rating based on feedback and achieved 88% graduation rate over 25 mentees in the class

Batch 2024 H2: Earned a 4.95/5.00 mentee satisfaction score and contributed to a 91% graduation rate for a 25-mentee cohort, reflecting improved mentoring strategies and class engagement

Optimized business plan supervision workflows by developing a Jupyter Notebook that streamlined location assessments and automated expense calculations, reducing analysis time by up to 50% and enabling 2–5 reports daily.

Conducted over 200 revenue projections and asset valuations for properties valued at more than $100 million, supporting strategic investment decisions and long-term profitability assessments.

Generated data-driven insights for investment strategy reports by combining internal performance metrics with external OTA data (Airbnb, Booking.com), improving stakeholder confidence in decision-making

Collaborated with the Revenue Manager to design and test time-segmented dynamic pricing strategies; developed a price adjustment automation tool that reduced pricing execution time by 2× and enabled faster, more responsive strategy iterations

Implemented a data analysis research framework to address statistical and data-driven challenges

Applied Data Analysis to provide recommendations on top-performing classes, most active mentors, preferred industries, and competitive pricing based on Jobhun’s industry partners’ products