The Forecast+

I may transition to Product Management roles, with a longer term plan as a CTO someday.**

There are still many large industry sectors that aren’t even on the cloud, let alone able to use ML in an impactful capacity. There are also many problems that are touted as solved by an “AI”, which are clearly not.

I want to work in tech strategy, especially for emergent tech, because I want to bridge the gap between solving real problems using cool engineering and merely generating sales through buzzwords.

** Life may cause operational turbulence or disruptions.

The Outline+

Starting August 2023, I will be enrolled in the MBA program of the Wharton School of the University of Pennsylvania. After nearly 4 years at Google, this is not an easy decision. But I consider this time well-invested to sharpen the skills I’m learning at Charter Space and at Google with a formal understanding of finance and operations. I believe that this will augment my technical ML and tech consulting skills for a career in product management.

I also want to use this time to understand entrepreneurial markets outside of the United States, and specifically those in emergent technology and its auxiliary operations. This includes generative AI, but also AI ethics and the creation and management of research policies that help shape the multimodal AI features that we intend to use to solve complex real-world problems.

At this time, my chosen majors are in Entrepreneurship and Innovation, Operations and Decision and Finance. I hope I have more to add here once school starts in earnest and I can showcase cool projects I hope to be a part of.

The Interstellar Radio+

During the COVID-19 pandemic, I was lucky to make close friends who were on the cusp of starting their space logistics startup. With Google’s permission, I’ve been fortunate to be a part of Charter Space’s founding team where I’ve had to wear many hats and learn about the space logistics industry.

Space travel is not only the next footprint of humanity but is also an important component in monitoring climate support, maintaining greater connectivity and supporting international security. Old and new space companies need to be prepared to be able to send critical missions without delays.

What did I learn: How to run operations at a small startup, how fundraising works, how does a software team interface with space engineers, critical concerns in the space industry around orbital pollution, managing regulatory concerns into product development and how to hire people.

The Archives+

Where: Google Cloud, NYC

My origin story of being hired at Google started with the Recruitment/Networking event hosted by Google PAIR. I was scheduled to take an interview as an ML Engineer at the time, until I met a recruiter after the panel. She looked at my resume and said, “Wait, if you have comedy experience why aren’t you in a more client-facing role?” And that’s how I was hired into Google Cloud Consulting as a Cloud Engineer.

My last few months have Google have been with the Ads Automotive Strategy team. This team has a global focus in shaping advertising strategy to harness the growing interest in Electric Vehicles. My work has also shaped how Google’s generative AI solutions can help automakers shape their content strategy in the future.

What am I learning: How merging organizations streamline operations at scale, managing executive stakeholders and enterprise-scale transformations. Also, how the tech landscape adapts to the use of Generative AI solutions across development, implementation and customer-facing operations.

Read more about my journey at Google here.

The Manuscripts+

At IBM Watson Health, I worked in a software engineering team that worked with IBM Research to create a prototype product deployed across a hospital network of nearly 875 Primary Care Physicians (PCPs).

It was designed to increase the time doctors were able to spend providing personalized care, without using their valuable time for data trends across the patient’s healthcare history.

What did I learn: How to take a research paper and create a product around it, what HIPAA means and the challenges of making healthcare accessible without burning out doctors.

My experience at IBM also taught me that ML-transparency and literacy aren’t as clear to customers whose problems we’re trying to solve. I wanted to work more with the client and people-interfacing parts of building ML.

Also I spent a lot of my time outside of work making comedy a serious hobby.

The Fossil Record+

My time at SEAS was focused on working in CS research and taking as many ML classes as I could (Statistics, Machine Learning and Statistical Machine Learning).

I also took classes in Linguistics, Japanese History and African Philosophies and Cultures. These classes formed the bedrock for how I read, perceive and study culture.

What did I learn: The technical rigors of school aside, these formative years taught me how to manage my time and health, to live alone as an adult and to plan for the future.

Read more about my hackathon journeys and my research experience here.