A record of GM-level product leadership: owned a $45M+ portfolio of B2B SaaS, B2C e-commerce, and data products through post-acquisition integration, market contraction, and the operationalization of GenAI — delivering 2× the business unit’s growth rate while expanding addressable market by $143M+. Built on customer discovery, pricing power, channel creation, and the discipline to cut what doesn’t work. The playbook is domain-agnostic; the judgment that drives it isn’t.
I lead products with a P&L lens. The work is to make a portfolio durable — not just to ship features, but to compound revenue, retain customers, and open the next market before the current one slows.
My career has been a steady climb through the same kind of problem at growing scale: take a product or portfolio that should be growing faster, find the structural reason it isn’t, and rebuild around it. From one flagship product to a multi-product portfolio. From IC to Director to Senior Director with full P&L. From renewing existing customers to opening new countries, new commercial models, and new addressable markets.
I’m an academic by training (PhD, former professor) and an operator by practice. That mix shows up as comfort with ambiguous data, technical product domains, and stakeholders who want both rigor and speed. I’ve run product through a global pandemic, a post-acquisition integration, a contracting enterprise market, and the GenAI transition — and grown the business through each.
Each step has added scope: from a single product, to a flagship, to a multi-model portfolio with full P&L. The pattern is consistent — find the commercial lever others have missed, build the operating muscle around it, then hand off something that compounds.
Molecular genetics, NSF and National Geographic-funded. Dolphin field research across four continents. 14 peer-reviewed publications →
Curiosity drives the question. Data drives the answer. Hypotheses get revised when they’re wrong. The same posture I used in the lab is the one I bring to a P&L.
The most durable portfolios are organized around a core content or data asset with multiple commercial layers stacked on top — each reaching a different buyer at a different price point. The Clarivate portfolio I led for six years was structured this way: workflow tools and partnerships fed the asset; subscription, e-commerce, and bulk services took it to market. Strengthening any inflow strengthened every outflow.
Premium workflow software for the upstream supply chain — submission, review, archiving, and impact analytics for contributing partners. Both a product and a content-acquisition channel.
The central commercial hub: subscription database licensed to enterprise customers, with a citation/index uplift distributed via partner platforms and a GenAI research layer that opens new buyer segments.
Direct-to-consumer purchase of derived content products. Print-on-demand and commemorative purchases reach a buyer the enterprise channel can’t economically serve. Built one storefront 0→1 to $1M run-rate.
Transactional and licensable use of the same content asset: bulk PDF delivery to large institutions, custom data extracts, API access for downstream analytics. Higher contract value, longer sales cycle.
Country- and segment-level partnerships that both expand the content moat and unlock markets the standard commercial model can’t reach — including a $143M+ TAM in a sovereign market.
Three years owning the P&L of a portfolio I’d worked inside since 2019. Inherited a $39M portfolio with a $5M unprofitable services line dragging margin and forecast quality. Delivered $45.8M with 70%+ EBITDA — after sunsetting the unprofitable services line, during an enterprise SaaS market contraction, on a continuing portfolio that grew at 2× the BU’s blended rate.
A note on the numbers. Final portfolio composition ($45.8M) reflects end-of-tenure scale to the best of my recollection. The inherited 2023 portfolio size (~$39M) is an estimate based on personal recall and the disclosed YoY growth rates — I no longer have access to the underlying data. Percentage figures, renewal rates, partnership counts, content acquisition metrics, and YoY growth rates are pulled from my own records and performance reviews.
All six moves are from the same portfolio (ProQuest → Clarivate, 2019–2026). The portfolio followed me through an acquisition and a scope progression. The repetition is the point: compounding only shows up when one operator stays at one P&L long enough for the decisions to connect. Each move is told as judgment, not a feature list: the read on the market, the call I made, what compounded.
Most BUs were defending share. We took a price uplift — +40 bps above BU average — on the installed base while contraction-era budgets were being cut around us. The lever was positioning: the flagship was renewed as mission-critical research infrastructure, not a discretionary line item.
The judgment In a contraction, what survives the budget review is what customers can’t do their job without. Get the positioning right and the renewal — and the price uplift — follow.
Two channels stood up in parallel. B2C: the brand was capturing institutional wallet but not individual wallet. Acquired a domain, built a new storefront, activated PLG on the existing one. APAC: a transactional market restructured into recurring ARR — $4.8M ACV growth.
The judgment The asset existed; the channel didn’t. New channels are usually a packaging problem dressed up as a product problem.
The GenAI capability wasn’t framed as a price-uplift lever — it was framed as a market-expansion lever. The technology delivered workflow compression that opened buyer profiles previously priced or positioned out of the portfolio. Validated PMF on the flagship; mapped segment-specific willingness-to-pay to buyer JTBD.
The judgment The right question on AI isn’t “can we charge more for it.” It’s “who couldn’t buy us before that can now.” That’s where the compounding sits.
Content acquisition was a KPI before I built it — not a function. Stood it up: 200% supply-chain scale, 450K+ records (54% international), 12 top-ranked universities through novel acquisition strategies. The proprietary content base is the moat — and it set up the cross-platform integration that delivered $25M of EMEA pipeline on a different product.
The judgment The moat in data products is the rights, not the software. Partnerships are how you widen it faster than competitors can copy — and how you compound it across acquired customer bases.
The market wasn’t a translation problem and the commercial team couldn’t reach it directly. The reseller partnership delivered local sales force, distribution, and institutional relationships we didn’t have — reaching 150+ institutions and 14,000+ potential customers, plus 300K acquired content assets. From there, mapped $25M of non-US ARR whitespace beyond.
The judgment In regulated markets, you don’t out-build access — you partner for it. Get the structure right and the product follows.
A legacy services line peaked at ~$5M. Project-based, unprofitable, unforecastable. Cut it, end of 2024. Replaced with higher-margin recurring streams: B2B ARR growth, citation-index price realization, B2C expansion, APAC ARR conversion. EBITDA roughly doubled. The continuing portfolio grew on top.
The judgment The portfolio was carrying revenue that wasn’t worth the volatility. Cutting it was unpopular and correct.
The product strategy is downstream of the financial strategy. I start with where the dollars need to come from in 18 months, work backward to the commercial model, and only then decide what to build. Roadmaps that don’t map to a revenue line are theater.
The hardest call in product is what to stop doing. I’ve killed lines that still generated revenue because the math was wrong, then redeployed the team into something that compounded. Healthy portfolios shrink as well as grow.
Most companies treat renewal as defense and net-new as offense. I treat them as the same motion at different time horizons: the customer who renews at +3.6% this year is the customer who buys the second product next year. Retention is where compounding starts.
The fastest way to coordinate thirty people is not more meetings. It’s making sure each of them can finish the sentence “we’re building this because…” the same way I would. When everyone holds the same north star, decisions get made at the right altitude: I don’t need to be in the room for the team to make the right call. Strategy that doesn’t translate into autonomous front-line judgment is just a deck.
The work runs on communication: early reads, working drafts, the willingness to be talked out of something before it ships. People do their best work when they’re contributors to the vision, not recipients of it. When the call has to be made I make it. The rest of the time, the room is smarter than I am, and I’d rather use that than perform authority.
Outward: I use AI to drive pricing power, expand markets, and unlock new revenue. Embedding ML and GenAI into established products to lift adoption, strengthen renewals, and generate pipeline. Inward: same instinct, turned on the operating model. Compress decision cycles, automate reporting, kill the meetings that should have been a dashboard. Leaders reasoning about AI from the outside age fast.
I want everyone on the team to be able to point to the work we shipped and say “I helped build that.” Not because I delegated tasks, but because they helped shape the destination. The buy-in isn’t a deliverable. It’s the whole game. — Operating note, 2025
When the right scope shows up — typically 3–6 months with a B2B SaaS, data, or AI-product portfolio that needs P&L-grade product thinking and someone who’s done the kill decisions — I take on fractional, advisory, or board engagements alongside the search.
Working as Product Confidential. Same playbook, scoped differently.