Tolu
Head of Product
I build AI-powered & AI-native products that help businesses grow — and I build the organizations that ship them. At Meta, I've led a team of product managers scaling Meta Business Suite from under 5M businesses to over 30M, driving multi-billion dollar revenue impact. My scope has spanned the full business lifecycle — identity and access management, business setup across Facebook and Instagram, content creation, advertising, and most recently, AI-powered tools that help businesses create posts and ads. Before Meta, I took OYO's US product from zero to $20M in revenue in year one, building the mobile & web app from scratch and growing the user base to 500K+. Earlier at Cratejoy, I was the first product hire post-YC — I built the product and growth team, launched a marketplace from zero, and helped scale revenue to $50M+ by year four. The thread across my career: I find the user problems that matter most, then build and scale the product org to solve them. I'm drawn to complex, multi-sided platforms where the intersection of product strategy, data, and organizational design determines who wins.
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Verified Interview Moments
Real responses captured live in AI practice sessions — evidence no resume can fake.
Alex asked
“How did you go about that, getting them bought in on the change?”
“For leaders, I needed to understand what their incentives are and what they care about. And for them, it's about the increase in revenue, bottom line for the company. So, helping leaders understand that we could be moving faster if we had a classifier that had higher precision and recall... which meant learnings came a lot faster.”
Alex asked
“tell me about a time you had to work with a difficult team member. How did you handle that situation?”
“I tried to articulate to the person... the value of moving fast is really to gain learnings quickly so we can prove or disprove, rather than wasting time. And helping them understand that this is also beneficial for their engineering team so engineers are actually delivering value... rather than spending their time polishing things up that maybe would not have any impact.”
Jordan asked
“could you tell me how you'd design a system to handle millions of users?”
“Give me an example of uh what type of system you mean, like a consumer or business uh product.”
Packet Strength
+23 to the next milestone (75)
Momentum building — the climb is on
Gym badges
AI Proficiency
Orchestrator
Chains multiple AI tools, builds complex prompts
Tools used
Practice Trajectory
Sessions build the signal — every score below is progress toward the next milestone.
- Verified sessions
- 0Verified sessions
- Average score
- 0%Average scoreBuilding · +12 to 50
- Best score
- 0%Best scoreStrong
Focus areas
tick = next milestoneBuildingClimbingStrong
Demonstrated Skills
- Stakeholder Management92%
- Revenue / Business Impact Framing92%
- ML/Classification Systems90%
- LLM-assisted Labeling90%
- Cross-functional Influence (without authority)90%
- Experiment Design / A/B Testing85%
- Product Sense / MVP Thinking85%
- Monetization / Upsell Strategy82%
- Conflict Resolution80%
- Growth & Activation Strategy80%
Also claimed
Verified skills are backed by AI-assessed practice evidence, not self-reporting.
Cross-Domain Experience
Stakeholder/Leadership Communication + Product Management + 3 more
Cross-domain engineers bring unique perspective connecting 5 areas
Technical Judgment
Ability to verify AI outputs, catch errors, and validate assumptions — measured across practice sessions.
Healthy skepticism
- Recognized that the existing classifier's root problem was definitional (weak business definition), not just a modeling issue — showed systems-level diagnosis
- Understood that poor precision/recall in upstream classification cascades into experiment dilution downstream — demonstrated causal reasoning across ML and product experimentation
- Chose LLMs for labeling as a pragmatic bootstrapping mechanism rather than a silver bullet, paired with downstream ML training
Business context awareness
- Asked for context on whether the system is consumer or business-facing before proceeding — indicates awareness that design decisions depend on deployment context
- Linked classifier precision/recall directly to experiment dilution and slower learning velocity
- Connected classifier quality to monetization upsell targeting efficiency and conversion rate improvement
- Framed MVP approach to a skeptical engineer in terms of engineering team velocity and value delivery over time
Technical judgment indicates how well this person evaluates solutions, catches issues before production, and connects technical decisions to business outcomes.
About
Tolu Babalola is a dynamic Senior Product Leader with over 15 years of experience in driving digital transformation and building enterprise-scale digital platforms. He has proven expertise in integrating secure identity solutions and crafting customer-centric digital experiences, adept at leading cross-functional teams to deliver breakthrough strategies in fast-paced environments.
Key Projects
Meta Business Suite
@ MetaHead of Product · August 2020 - Present
Responsible for building the end-to-end foundations of the Meta Business Suite, enabling 40 million SMBs to manage their profiles and run ads across Meta’s Family of Apps.
OYO Product Launch
@ OYOVP of Product · May 2019 - August 2020
Launched and scaled OYO’s digital platform for the US and UK markets, enhancing customer engagement through integrated booking systems and dynamic pricing models.
CrateJoy Marketplace
@ CrateJoyHead of Growth & Product · June 2015 - May 2019
Transitioned CrateJoy’s B2B SaaS product into a dynamic digital marketplace, driving a new revenue channel through integrated digital experiences.
Cater2Me Digital Platform
@ Cater2MeSenior Product Manager · March 2014 - May 2015
Led the design and launch of a digital platform connecting corporate clients with food vendors, focusing on user experience and operational security.
College2Startup
@ College2StartupFounder · April 2008 - November 2013
Founded a machine learning-driven job board connecting college students and recent grads to startups, scaling the company to $3M in revenue at the time of exit.
Notable Projects
Business Entity Classifier Rebuild
Identified that the existing ML-based business classifier (trained on third-party human labels) had poor precision and recall due to weak definitions and labeler inconsistency. Led initiative to replace it using LLM-generated labels with a clearly defined business definition, then trained a new ML model on those labels.
Precision improved by over 50%; recall also significantly improved. Experiments became cleaner (less diluted), learnings accelerated, and upsell/monetization conversion rates increased.
Communication Style
Structured and methodical. Tolu frames technical problems in business outcome terms (revenue, experiment velocity, conversion rates) when speaking to leadership, and shifts to engineering velocity and team impact when addressing engineering partners. Uses clear hypothesis-driven language and demonstrates empathy for different stakeholder incentives. Slightly verbose but substantive.
Leadership Signals
- Led net-new ML classifier initiative end-to-end without formal authority over engineering or data teams
- Aligned multiple teams across an ecosystem by connecting a technical deficiency to their individual incentives
- Convinced senior leadership to invest in rebuilding a core classification system by framing impact in revenue and experiment velocity terms
- Managed a difficult engineering partner by negotiating MVP scope with explicit commitments about polish before full rollout
- Advocated for hypothesis-driven, iterative product development practices across teams
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