An experienced and knowledgeable venture capital investor with an industry reputation for pragmatism and critical-thinking. After nearly a decade of investing in emerging technologies and the North American venture capital funds, I decided to pursue graduated studies at Northwestern University to develop technical skills that serve the demands of an ever-increasing digital world. The opporunity also allowed me to partner in an entreprenerial opportunity. Through my formal education and consulting projects, my data science skills have greatly enriched my capacity to deliver actionable data-driven insights and build predictive models. Currently a student, freelancer, blogger, and startup board member.
Hired initially as an analyst to conduct due diligence, business development, manage portfolio data, and improve systems for venture capital fund opportunities in Canada and the US. Sourced and lead transactions totalling $15M in equity commitments approved by the Board of Directors. Seconded the financing of over $150M of equity commitments to venture capital funds. Represented BDC as a member of 10 Limited Partnership Advisory Committees. Played a principal role in designing, modelling, and fundraising the $1.2B Venture Capital Action Plan initiated by the Canadian Department of Finance. Modernized data management by leading and implementing a portfolio monitoring SaaS platform. Developed predictive analytic models using the COTS CRM API to identify the best opportunities.
Hired as an intern to help launch a COTS private equity portfolio transaction and portfolio monitoring for the management of the $2B billion private equity portfolio processes. Contributed to the due diligence and analysis of direct private equity and venture capital deals. Lead a cross-department IT project as the business team principal where I coded custom reports.
I am an advisor and board member a well-funded startup in stealth mode. Due to the NDA in the shareholders agreement, inserted by a major investor, I am prohibited from sharing details of the company's business.
Full end-to-end machine learning model trained on 30 years of advanced statistical data. The data scraped from basketball-reference.com, wrangled, EDA, shaped, and scaled. The model was tuned and tested with 8 different machine learning alogrithims. The selected model yeilded 99% overall accuracy and a 93% All-NBA and team selection accuracy.View Project
Since Google and the Government of Canada were late to produce an easily accessible interactive dashboard for finding COVID-19 statistics by province, I have created one with Plotly and DASH using daily statistical information.View Project
I enjoy writing and will occasionally share some of my thoughts on my personal blog linked below. It is a heterogeneous blog with posts on basketball, business, data science, python libraries, and politics. Sometimes these are stream of concious writings, other times I am a little more contemplative. Either way, caveat emptor and don't judge too harshly.Jump To Blog