🎯 Mark Freeman II

onthemarkdata.com | Senior data engineer helping brands with GTM and content strategy.

I'm a community health advocate turned data engineer interested in the intersection of social impact, business, and technology. My life’s mission is to improve the well-being of as many people as possible through data—especially among those marginalized.
 
As the founder of On the Mark Data, I use this platform to help brands connect to data professionals through captivating content.
 
I received my M.S. from the Stanford School of Medicine and I'm also certified in Entrepreneurship and Innovation from the Stanford Graduate School of Business. In addition, I have worked within numerous startups where I have put machine learning models into production, integrated data analytics into products, and led migrations to improve data infrastructure.
 
The culmination of my technical skills, content success, and experience within startups would make me an asset whether you are looking to create a successful content strategy, engage in influencer marketing campaigns, or develop a go-to-market strategy for your startup.

You can learn more about opportunities to collaborate, my media appearances, or my content at onthemarkdata.com.

Tech Lead, GTM Engineering

• Joined as employee one to lead GTM at Gable to help us reach our first customers.
• Worked with the CEO to establish our GTM strategy, content strategy, and messaging for technical audiences.
• Building internal data products to drive our GTM strategy and sales.

Hannes Mühleisen (co-founder of DuckDB) doing his slides like SQL queries is so damn cool.

The most powerful communication is often what's not said.

Dev 🤝🏼 GTM | O’Reilly Author | [in]structor (33k+ Learners)

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Joe Reis recently inspired me to consider Claude Code as a writing tool when he challenged the notion of context graphs.

He essentially took the Foundation Capital article that has been going viral and ran it through Claude Code.

It instantly transformed a very theoretical article into an entire code repo you could explore on his public GitHub (then used that as a tool to critique).

A thought then popped into my head:

"What if my writing was so clear that you could throw an entire article into Claude Code and it would one shot a working POC of the presented ideas."

Welp, the implementation chapter of our book (Ch. 7) is public on GitHub and is stored as a README file.

So let's see if my writing is clear enough for AI to one shot it.

I downloaded the repo to my local machine, got rid of every file except the README, the JSON dataset, and a folder of diagrams from the chapter.

Then I prompted: "This is my technical chapter of the book. Implement it."

I couldn't believe it... In about 10 minutes it was spinning up a Docker image, loading data into Postgres, and then running my tests.

Keep in mind I spent a couple intense weeks building that codebase for the book.

To be clear, I'm not advocating for such code to be useable, but rather it's now a tool that I can use to further validate my writing.

Present your novel ideas.
But show the working POC.

It's the same as in academia where you have research papers that include code and data for repeatability.

The bar has risen for everyone now. Talk is cheap.

Crazy Idea: I use claude code on a live stream where I'm building an app from scratch.

But...

People on the live stream can enter prompts on my behalf while I'm building to either help or mess with the project.

I think the chaos would be both fun and hilarious.

You didn't hear it from me, but the coolest talk at Data Day Texas is happening at 2:55PM in room 204 👀

Live coding exercise for my conference talk at Data Day Texas? You bet!

Saturday - 2nd Floor - Ballroom 204

Code: The Untapped Metadata Source Driving Most Data Failures

Over the past three years, collaborating with organizations implementing data contracts and writing the O'Reilly book on the topic, it has become abundantly clear that data is surprisingly the wrong surface to address systemic data issues —we actually need to focus on code. Code not only defines what data is captured and moved through software systems, but it's also the ultimate source of truth in how a business operates and its documented changes over time. This is evident in how some of the most significant internet outages can be traced not only to code changes, but also to how those changes affected underlying data models. In this talk, I share case studies and lessons learned in extracting metadata from code and using it to enforce expectations across software systems and data products.

Remember back in the ancient internet times when you could use this cool thing called HTML to customize your Myspace profile.

Just randomly hacking away at sections to see what changed. Copy and pasting random code and hoping it works.

Vibe coding feels eerily similar.

What data scientist experienced in 2023 with the realease of ChatGPT is the same experience software engineers are experiencing today with Claude Code.

Back in 2022 I was transitioning from data science to data engineering, and a major project that led to that promotion was building and deploying a natural language processing pipeline.

Back then I was using SpaCy, which was an opinionated framework for implementing the latest production-ready NLP models.

Yeah... I open up ChatGPT and immediately see the "advanced" work I put into production was now antiquated.

While I wouldn't throw an LLM at my production project back in 2023, I was intimately aware of what was required to get business value out of text.

What ChatGPT showed me was that a large portion of the difficult parts of NLP were now just a simple API call... and more importantly, highlighted the inflection point if the momentum of change followed.

Fast forward to today, and so many software engineers built side projects over the holiday break and experienced the exact realization I had back in 2023.

"This LLM accomplished way more than I expected, and I can see an inflection point if this trend continues."

Imagine what will be accomplished in the next few years.

Finetooth Analytics

Finetooth predictably answers go-to-market questions and visualizes alternative scenarios to optimize marketing strategy and business outcomes.

Gen AI Advisor

T-Mobile

Lead the development and implementation of a centralized metadata tracking system for T-Mobile's Contact Center AI (CCAI) and predicted ML pipelines, leveraging Databricks, Snowflake, Unity Catalog, and Power BI to standardize 100+ ML processes and enable real-time analytics. 

Systemized metadata labeling and built a feature store, improving operational efficiency by 30%, ensuring SOC 2 compliance, and supporting 10M+ customer interactions monthly (120M+ annually) through automated data handling and enhanced data governance. Reduced processing time by 30% and scaled model deployment, increasing predictive accuracy across hundreds of ML models and millions of interactions annually.

Lead MLOps Engineer & Data Architect, Customer Care

Data Science Manager, Global Business Marketing

Google

Google Cloud Next 2023 featured project - Selected to present self-developed innovative marketing generative AI project at Google Cloud NEXT 2023.

Developed a generative AI model that hyper-personalizes marketing and advertising. Was selected as a finalist for the Google Cloud Hackathon.

Handpicked to volunteer as one of Google’s private testers for the closed beta of Google Cloud’s Generative AI for Vertex AI. Vertex AI's managed endpoints make it easy to build generative capabilities into an application, with only a few lines of code and no ML background required. Developers can forget about the complexities of provisioning storage and compute resources, or of optimizing the model for inference.

Featured AI Marketing Technologist, Google Cloud Next 2023

UiPath

Lead the marketing analytics and engineering team to produce complex data models, automation, A/B tests, and statistical hypothesis tests using SQL, SOQL, and Python in support of 1,058 sales and marketing stakeholders including Account-Based Marketing (ABM) and Partnerships.

- Oversee all global marketing performance analytics (lead management, campaign performance, demand generation outcomes) and present a clear demonstration of the global marketing team's impact on revenue to the CMO, CFO, and the Chief Business Officer.
- Develop pipeline forecasts and revenue governance with the VP of Marketing and CMO to achieve a margin boost of +42%. Achieved a 30% marketing ROI ($390M out of $1.3B ARR) and revenue CAGR of 27%.

Director of Marketing Analytics & Data Operations

Eliminated technical debt by 40% by designing a new data architecture model, including automated production-grade data pipelines, data contracts leveraging the transactional outbox pattern, a contract request system, CI/CD, and versioning to address the business’ most critical use cases.

- Migrated a data warehouse from Google Cloud to Microsoft Azure, including services (APIs, etc.), frameworks, taxonomies, and best practices to deliver mission-critical sales, marketing, and Netsuite ERP data.
- Transformed marketing data from Marketo, Salesforce, Clari, Netsuite, Google Analytics, product (Mixpanel), third-party data (Bombora, Uberflip, DNB), and media into complex SQL data models (CTEs, subqueries, recursion, window functions, etc) and generate insights to optimize multi-touch attribution, conversion rates, retention, cross-sells, upsells, and A/B tests.

Senior GCP Cloud Engineer

Fortune

Operate as a fractional senior executive and advised the CTO, VP of Marketing, and Product on a CRO playbook that is projected to optimize lead conversions from 33% to 49% for Fortune Connect.

- Engineer a measurement playbook for the first time in two years to course correct and optimize leads, a website, email nurture, and PPC ads to pace toward $250K MRR.
- Direct product experiments, averaging 26.3 experiments per month with a 49% success rate, resulting in projected gains of +131% in subscriptions and projected gains of +231% in D14 and D30 retention.
- Lead transformation of management culture to metrics and analytics-driven practices. Implemented processes and procedures to monitor and analyze the business, advise and provide guidance to management, and plan and implement business decisions.
- Manage daily operations including refining cost models, budget planning & management, analytics, PMO, acquisition integration, audit and compliance, and partner/vendor management.

Senior Data Scientist, Marketing

Wiley

Contributing writer to the 3rd edition of Data Science for Dummies (2021). 

Discussed data science topics, including multivariate testing, econometrics, granular media mix models (MMM), and multi-touch attribution (MTA) to measure marketing ROI.

Contributing Writer, Data Science for Dummies

EquityMultiple

Responsible for global marketing performance evaluation (lead management, campaign performance, demand generation outcomes) and clear articulation of business impact at CMO staff meetings and quarterly business reviews with the COO.

- Established global marketing analytics practice which syndicates capabilities from corporate to regional teams through playbooks and new dashboards on measurement planning, execution, and course correction. 
- Built ML-based ROAS solutions such as conversion lift studies, brand insight tools, incrementality tests, and sales forecasting (ARIMA and Robust regression).
- Served as the technical authority on Customer Data Platforms, lead personalization, integration, and data migration.
- Developed, tested, and deployed a real-time investment recommendation engine incorporating collaborative filtering and content-based filtering techniques to target leads and automate sales using an ID graph based on first and third-party data. It is projected to add $100M+ in revenue and exceed goals with a +17% margin improvement.
- Successfully built and scaled a customer data platform to support a B2C programmatic media buying operation, resulting in a +5x increase in ROI from a marketing budget of $3M.
- Programmed, tested, and deployed automated multivariate regression tests using scalable ETL processes, a Kubernetes cluster, and Kubeflow that tested and optimized a multi-touch attribution model.
Shaped CxO-level agenda on digital marketing and growth, resulting in an increased CLTV by +30%.


Key Tech: SQL, Python (Numpy, pandas, scikit-learn, Matplotlib), Machine Learning algorithms (Decision Trees, Random Forest, Regression, Convolutional Neural Networks, Principal component analysis, Clustering, Naive Bayes, Dimensionality reduction), Data Wrangling, Data Mining, Terraform, Google Cloud, BigQuery, Redshift, Terminal, Docker, Postman, Tableau, R Shiny, Design thinking, Bayesian inference

Head of Marketing Data Science & Analytics

Okta

Developed and executed data science models aimed at increasing the efficiency of marketing sourced revenue through predictive LTV models that surfaced marketing activities highly correlated to late-stage sales conversion. Shortened sales cycles by 20%, increased AOV by $40%, and incremental ARR profitability growth of 30%.

- Created a self-optimizing lead scoring algorithm that identified qualified leads with sales intent and high AOV. 
- Boosted sales conversions by +30% after using insights in cross-channel campaign management (CCCM).
- Built, trained, scored, and tuned a multi-touch attribution model to identify gaps in the MQL funnel from the lead source to Sales Qualified Opportunity (SQO).
- Developed optimization and simulation algorithms scenarios to help provide the marketing investment and allocation recommendations to stakeholders;
- Scaled current Python modeling algorithm through parallel processing and other enhancements;

Key Tech: SQL, Python, Snowflake, Marketo, Salesforce, OLS, Predictive Modeling, Statistical Analysis, Data Wrangling, Data Mining, Terraform, Google Cloud, Terminal, Docker, Pattern Recognition;

Cellebrite

Owned marketing operations, analytics, business intelligence, lead management, attribution, and conversion rate optimization (CRO) to launch enterprise marketing programs for North America, EMEA, UK, APAC, and LATAM.

- Established a single source of truth in marketing/revenue operations by aligning sales and marketing teams around a single view of the business with shared revenue targets and KPIs (i.e., pipeline velocity, sales cycle time, win rates, CAC, ARR, attrition, LTV, forecast accuracy).
- Partnered with cross-functional GTM teams to define quarterly and annual demand generation targets, monitor achievement, and deliver forecasts that will help teams course-correct.
- Developed ABM and persona-based audience strategies to achieve demand generation goals and support marketing automation and paid media teams with audience targeting.
- Demonstrated CAC, CLTV, ROAS, marketing-influenced pipeline, and revenue impact from marketing activities (lead management and campaign performance) in monthly reports and QBR presentations to the CMO and the GTM team.
- Successfully drove $1.2MM in cost savings by partnering with cross-functional teams and conducting data analysis to identify areas of improvement within the marketing technology stack.
- Led the successful implementation of a new lead management system (the Sirius Decisions Demand Waterfall framework) which resulted in a 10% increase in leads converted to customers.

Key Tech: Qlik View, Qlik Sense, Alteryx, Redshift, SQL Server Management, Eloqua, Salesforce, Excel, Python, SQL

Global Manager, Marketing Technology & Marketing Operations

✨ MISSION & VISION ✨
I like to think of myself as a visionary who bridges the gap between analytics expertise and delivering business value. And, while I strive to be that guy, I truly define myself as a happy warrior who builds innovative teams to drive cutting-edge revenue optimization strategies for high-velocity, promotional, and data-centric brands. I stay close to what’s trending to remain fresh and engaged with the consumers I market to.

As a data leader, I recognize talent and build culturally relevant organizations from the ground up. I truly believe that the more exposure teams get, the better prepared they’ll be to face the emerging business landscape and positively affect change.

👨🏽‍💻 WHAT DO I DO? 👨🏽‍💻
I help executives connect business activities to revenue, drive customer acquisition, and shorten sales cycles. I surface what companies can do to drive the revenue outcomes they want to see.

I wasn't the kid who couldn't surf dial-up internet because his Mom was on the phone—I was the kid who wired phone cables from scratch and made it work. Since then, I haven't stopped taking on impossible, beat-your-head-against-the-wall data challenges.

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Kam Lee 📈

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Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}🎯 Mark Freeman II's LinkedIn Performance

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Performance Score🎯 Mark Freeman II's LinkedIn Performance