Hi. Welcome to my website.

​My interests include technology, startups, investing, finance, strategy, product, data science, and customer analytics.

I write long-form posts on this website. I also write a newsletter called Under the Curve.

​Ten Second Bio

  • Bay Area, California
  • Director, Growth & Data Scientist at an AI startup
  • Former portfolio manager at a $1B investment firm
  • CFA; Wharton MBA
  • Career also includes strategy at Samsung Techonology and Advanced Research, product at a hyperloop startup, data science consulting for the world’s largest media advertising company, and growth/analytics consulting for an e-commerce startup.


steve dot lastname @ gmail
Or reach out on LinkedIn.


Investing / Business

Investing is a beautiful collision of strategy, psychology, finance, math, and business, among other things. It requires lifelong learning, patience, self-awareness, insatiable curiosity, and ambition.

It has been my primary lens to view the world, and myself, since I was a teenager. I am a purest at heart, and was driven by the art of investing for a long time. It led me to study finance, and is why I spent nearly a decade at an investment firm honing the craft.

If I had to break investing into two parts it would be: 1) the art and science of investing - the confluence of finance, math, psychology, risk, law, and other elements; and 2) the study of businesses - strategy, competitive dynamics, execution, management, M&A, and all the nitty gritty elements that go into creating and growing a successful company. To be a great investor, I believe you have to excel at both.

While I still think about and refine my ideas on investing (much of my writing continues to be about clarifying this thinking), I am spending this next phase of my career on the business side, learning and growing by doing, rather than observing from an investor’s 500 ft view.

Technology / Startups

Technology companies and startups are the domain I have naturally gravitated to. I’m always trying new software and consumer products, and my information diet is dominated by these topics.

Perhaps it is because I spent some time early in my career investing in old industries that I’ve run so hard to the other side. Or maybe it is just because I’m fascinated by technological advances and how companies make them happen.

Either way, there is something about creating new things and experiences that energizes me. Unlike Warren Buffet, I don’t want to bet on things not changing - I want to spend my time on the new.

Tech companies and startups are incredibly dynamic with high rates of change, can grow rapidly, and succeed and fail for numerous reasons. This offers unlimited fodder for deeper questions that I’m interested in and driven by:

  • How to launch products and create new markets.
  • How the right business model can change a company’s outcome.
  • How companies can grow in the most optimal way (profitable, enduring, and value capturing).

Growth / Customer Analytics

Growth as a function in a company is a term that is thrown around a lot in startup land with hand-wavey like meaning. To some it means a/b testing and running experiments to grow a metric. To others it is about growth loops and marketing tactics.

To me, those are just components of a bigger picture. Growth is how a company can best capture the opportunity it is going after and grow in an optimal and value-maximizing way. With a strong emphasis on the how.

The other great description I’ve heard for growth is that just as how finance is about measuring and optimizing the flow of capital into and out of a business, growth is about measuring and optimizing the flow of customers in and out of a business.

I love this because it meshes so well with another foundational learning of mine - that there are three distinct customer processes: acquisition, retention, and spend. I believe this is the ideal lens to view almost any business, both for managers and investors.

This framework means that the foundation of growth is customer analytics. By focusing on the customer as the economic unit, decisions can be centered on understanding and improving one or more of these three processes.

Data Science / Analytics Engineering

Data used to be the domain of IT. It was slow, boring, and inaccessible. In recent years, cloud storage/compute, easy access to ML models, and the “Modern Data Stack” have changed that. Data is now fast, accessible, and endlessly fascinating.

As someone who has always loved working with data to create knowledge and inform decisions, this evolution in the data ecosystem has been a treasure trove for me. At the analytics/data science layer, I’ve studied and worked in areas including statistical modeling, predictive analytics, and natural language processing. And it has also enabled me to go down the stack into analytics engineering including data ingestion, data warehousing, and data modeling.

One of my principal tenants has always been to do the work. Primary research over reading reports. Building my own models instead of relying on others. To me, this is the only way to truly understand and have differentiated and substantiated opinions. Whether it is working in growth or any other domain, being able to manage and understand the underlying data, as well as apply the right analytics and data science techniques, is a powerful source of value.


My go-to programming language is Python for the vast majority of my use cases (data science, analytics, data engineering, prototyping, feature development, etc.). I also write a lot of SQL and have experience with JS/TS, R, HTML/CSS, and am quick to learn whatever else is needed.

Until recently, I’ve never thought of myself as a programmer. But after reflection, coding has been one of my superpowers my entire career.

In my teens, I became an expert in excel and VBA, creating numerous models and programs, including an entire operations data management system for a publicly traded company.

During my equity research internship, I wrote web scraping scripts for our commodities analyst, saving him many hours of tedious work and earning myself credibility to take on higher-level research projects.

As an investor, one of my key advantages was in automating a lot of the grunt work such as data ingestion so I could focus on actual analysis and investing. I also designed and built my own real-time portfolio management system and various trading tools that powered my daily workflows.

Currently, as a data scientist and wearer of many hats at an AI startup, programming supercharges my value add. I’m not a software engineer by any stretch, but being able to code - and more importantly, to approach and solve problems with a programmer’s mindset - is an invaluable skill that elevates every other domain of interest.


If you’ve made it this far, it goes without saying that I like writing. I love to read, but without writing it feels like I just have my feet in the water of a flowing river - enjoying the ephemeral sensation but not fully harnessing its power. Writing is like dredging my own river and gaining control over my knowledge.

Writing forces the brain to synthesize data, connect disparate pieces of information, form new ideas, and think more deeply about a topic. It is active learning rather than passive learning; and while more difficult, active learning is approximately a bazillion times more effective.

Thanks for stopping by.