HP Tech Ventures

Evaluated startups for investment by analyzing data with Python and developing Snowflake ETL framework

Overview

Evaluated over 30 startups for HP Tech Ventures, identifying high potential investment targets by analyzing 50k+ data points with Python (Numpy/Pandas). Developed a Snowflake ETL framework that improved data management efficiency by 32%. Also utilized SQL, Databricks, and Excel automation for analysis.

The Challenge

The main challenge was processing and analyzing large volumes of disparate data to identify promising investment opportunities. We needed to develop a systematic approach to evaluate startups across multiple dimensions and create a data pipeline that could efficiently process and store this information.

The Solution

I developed a comprehensive data analysis framework using Python (Numpy/Pandas) to process and analyze over 50,000 data points related to startup performance, market trends, and growth potential. I also created a Snowflake ETL framework that improved data management efficiency by 32%, making it easier to integrate and analyze data from multiple sources.

My Thoughts

Working with HP Tech Ventures provided valuable insights into the venture capital ecosystem and the criteria used to evaluate startups for investment. The experience strengthened my data analysis skills and gave me a deeper understanding of how data-driven decision making can inform investment strategies.

Key Achievements

• Evaluated over 30 startups for potential investment opportunities
• Analyzed 50,000+ data points using Python (Numpy/Pandas)
• Developed a Snowflake ETL framework improving data management efficiency by 32%
• Created automated analysis workflows using SQL, Databricks, and Excel
• Presented data-driven investment recommendations to senior leadership