Python, Python, Python
My journey to Revenue Operations wasn't a straight line. I’ve learned no one takes a straight path anywhere.
I started on Wall Street, first in the trenches of investment banking and later co-founding a hedge fund. For years, I lived and breathed financial models, corporate strategy, and the intricate dance of capital allocation. We were good at it. We moved value around, arbitraging inefficiencies and making calculated bets. But a nagging feeling grew over time. While finance was expertly moving value, the tech world was creating it from scratch.
Seeing the growing power of machine learning, the plummeting cost of intelligence, and the ever-expanding capabilities of technology, it was obvious where the real value creation was happening. That's where I needed to be.
So, I dove in headfirst.
I became obsessed with machine learning, treating it not as a hobby but as my next professional evolution. I cut my teeth in Kaggle competitions and late-night hackathons, pushing myself to build novel and powerful systems. I devoured the latest research papers each week, developing my own proprietary data engineering and machine learning techniques.
I began to see the connections. The discipline of financial modeling directly translated to building complex system models. The strategic thinking from corporate development was the foundation for designing go-to-market motions. My 15 years in finance, operations, and biz dev weren't a detour; they were the perfect training ground for what came next. I started weaving together my skills in system design, GTM integration, revenue intelligence, process automation, and, of course, Python. I've been writing Python since 2009, been a Salesforce Administrator for 14 years (certified for the last 5), a software developer for a decade, and a HubSpot admin for the last four. This fusion of skills was all in service of one goal: building better growth engines.
RevOps, at its core, demands a unique blend of strategy, deep analysis, and engineering discipline to construct a powerful, efficient, and reliable revenue engine. My focus is on optimizing these engines by meshing GTM best practices with master data management, AI, and automation.
And here’s the secret, the thing that gives me an almost unfair advantage: pairing machine learning directly with revenue operations. It’s my secret weapon for unlocking exponential growth. Keep it between us. 😉
To wield this weapon effectively, you need the right tools.
For me, that tool is Python. It's the connective tissue that links disparate systems, transforms raw data into strategic intelligence, and automates the mundane to free up humans for high-value work. If you're serious about building the next generation of revenue engines, you need to move beyond the UI and start building programmatically.
Let’s look at the top libraries.
The 5 Foundational Python Libraries for Every RevOps Pro
These are the workhorses. The libraries you'll use day in and day out to wrangle data, automate processes, and extract insights, regardless of your specific CRM or tech stack. Mastering these five will fundamentally change how you approach and solve problems.
Pandas: Your Data Manipulation Powerhouse
If you learn only one library, make it Pandas. Think of it as Excel on steroids, but infinitely more powerful, scalable, and repeatable. At its heart, Pandas provides a data structure called a DataFrame, which is essentially a programmable table. For any RevOps pro, who lives and dies by data from different sources (CRM, marketing automation, product analytics, finance systems), Pandas is the indispensable tool for cleaning, transforming, merging, and analyzing that data.
My "Aha!" Moment with Pandas: I was working with a Series C company whose lead and contact data was an absolute disaster. We had data from Salesforce, a list from a recent conference, and user data from our product database. The field values were inconsistent, formatting was all over the place, and we needed to merge it all to create a single source of truth for our territory carving and lead routing rules. A VLOOKUP in Excel would have buckled and cried. With Pandas, I wrote a single, reusable script. It ingested all three sources, standardized the state and country fields, normalized the job titles into our defined personas, and merged everything based on email addresses. The entire process, which would have taken a team a week of manual work, now runs automatically in under 30 seconds.
Key Functions to Master: