Technology executive running technology, data, and product as one P&L. Three generative AI systems shipped to production, all still running, saving about $2.4M a year, with governance built in.
Three systems run live today: a semantic search platform, an ML ranking model on AWS SageMaker, and a generative content engine. Together they save about $2.4M a year, with PII controls and model risk review built in before any data reaches the API.
Engineering by training at NJIT and an operator by temperament, my path runs from program management at Morgan Stanley and iHeartRadio, through an Agile turnaround of a 120-person organization at Amazon's Quidsi, to seven years at Trusted Media Brands running product and technology together. Along the way the remit kept widening: product and UX, then engineering, then data, then AI strategy at the board level.
At Trusted Media Brands the digital portfolio grew from 26M to 85M monthly unique visitors and into the Comscore top 10 in lifestyle media, on a $20M budget across a team scaled from 30 to over 150 in four countries, with the data science and engineering organization built from zero. At The Krazy Coupon Lady the work turned to applied AI: three generative systems in production, an enterprise AI policy and model risk review, and cost per AI output cut 65% after launch.
Governance is not what slows AI down. Done right, it is what lets you ship.
Every system ships through the same discipline: real source data, a governance gate that strips PII and clears model risk, then a model in production. Select a system to trace it.
Own a full technology P&L of about 28 people across product, data, and engineering on a $4.5M budget, reporting to the CEO. Shipped three generative AI systems now in production, all still running, together saving about $2.4M a year, and authored the enterprise AI Acceptable Use Policy and model risk review behind them, adding PII stripping and anonymization before any data reaches the OpenAI API.
Directed a technology organization that grew from 30 to over 150 across four countries, on a $20M budget split $16M operating and $4M capital, reporting to the CEO and presenting quarterly to the private equity owner group. Built the data science and engineering organization from zero to 15 in 18 months, then shipped a yield optimization model worth $2M in net-new annual ad revenue. Grew the digital portfolio from 26M to 85M monthly uniques and into the Comscore top 10 in lifestyle media, cutting critical vulnerabilities from 14 to 2 and holding 99.99% availability.
Led product, UX, and engineering across Reader's Digest, Taste of Home, and Family Handyman, with eight direct reports and six product managers owning lines accountable for about $35M in digital revenue. Launched a unified identity and subscription product that added $5M in recurring revenue, and ran the Operations division, lifting programmatic fill rate from 65% to 88% for $3.2M in additional annual yield. Put the company's first automated ML workflow into production in 2017.
Converted a 120-person organization from waterfall to Agile and Scrum across technology, UX, and product, and stood up automated QA frameworks that cut production bugs 40% and reduced cost per contact 15%. Led the technical design to migrate the Quidsi data warehouse inside the Amazon firewall. Amazon Scrum Master certified.
Rebuilt music royalty payment for a platform of 50M registered users, leading a seven-person team to design the SQL schema and the ETL across disparate sources and make the payment flow auditable.
Program and delivery leadership across financial services and enterprise software, including lead program management coordinating 150 offshore developers on the KPMG eAudIT platform used by 30,000 employees.
Open to conversations about technology, AI, product, and operations leadership in media, consumer, and technology.
Open to: Chief Technology Officer, VP of AI, Chief Product Officer, and Chief Operating Officer roles · Remote and New York metro