Senior Product Manager · AI & Enterprise Platforms

Building the AI
foundation for
prestige beauty.

Johana Luna Portrait

I lead technology products for Unilever Prestige's eight brands, including Tatcha, Dermalogica, Paula's Choice, and K18. I co-lead our AI foundation with the CIO and VP of AI, and own the central technology budget, applications, and vendors.

400people on Claude, under governance built from scratch
3.5 hoursagent work that took three weeks per brand
8 brandsone shared technology and AI foundation
Black and white portrait of Johana Luna

Professional Experience

Mar 2022 — PresentUnilever Prestige

Senior Product Manager, Technology Portfolio Ops & AI Enablement

Jul 2026 – Present

Senior Business Systems Analyst & Technical Program Manager

Mar 2022 – Jul 2026

Co-lead the AI foundation for eight brands with the CIO and VP of AI. Established the portfolio's first AI governance and security controls with Unilever's AI and cybersecurity teams, enabling 400 people to adopt Claude, and co-organized the first AI Hackathon, now an annual event, with projects live in sales and procurement. Designed an AI agent that prepares product data for Google's AI shopping in 3.5 hours instead of three weeks per brand. Won software discounts of up to 30% by negotiating once for all brands, merged seven formulation systems into one PLM platform, and, as technical program manager, led commerce, ERP, and 3PL integrations across four countries.

Hourglass✦
Paula's Choice✦
Tatcha✦
Murad✦
Living Proof✦
K18✦
Dermalogica✦
Garancia✦
Hourglass✦
Paula's Choice✦
Tatcha✦
Murad✦
Living Proof✦
K18✦
Dermalogica✦
Garancia✦
Hourglass✦
Paula's Choice✦
Tatcha✦
Murad✦
Living Proof✦
K18✦
Dermalogica✦
Garancia✦
Hourglass✦
Paula's Choice✦
Tatcha✦
Murad✦
Living Proof✦
K18✦
Dermalogica✦
Garancia✦
Oct 2020 — Mar 2022Shutterfly

Senior Data Analyst

Built a model that flagged orders at risk of shipping late, avoiding $4.5M a year in expedited shipping and refunds. Led the move from Looker to Power BI, including the Azure data lake behind it, across 20TB of historical data.

2016 — 2019Relocation

Relocated from Colombia to the United States; completed the Lambda School Data Science & Machine Learning fellowship (2019 – 2020).

2014 — 2016Claro (Colombia)

Regulatory Audit & Automation Lead

Governed 650M+ monthly records for national telecom regulatory reporting; automated audit reporting, cutting cycles by 40%.

Education

2009 — 2014Universidad Industrial de Santander, Colombia

B.S. Systems Engineering

Core Foundation: Advanced studies in Enterprise Architecture, Database Management (Oracle/SQL), and Algorithmic Logic.

2019 — 2020Lambda School

Data Science & Machine Learning Fellowship

Strategic Case Studies

Agentic Commerce6 Brands

AI-ready product data for Google shopping

Designed an AI agent that reads each brand's storefront and turns it into the conversational product data Google's AI Mode and Gemini use to answer shoppers. Preparing one brand's catalog took three weeks of full-time work; the agent does it in 3.5 hours, across ~1,175 SKUs in six brands. Paired with a rebuild of first-party tagging and conversion signals.

AI agents / Merchant Center / First-party data
AI Enablement400 People

Making AI safe to use, then useful

Established the portfolio's first AI governance and security controls with Unilever's AI and cybersecurity teams, enabling 400 people across Unilever Prestige and its brands to adopt Claude. Co-organized the first AI Hackathon (150+ people, 17 teams), now an annual event; projects now live include a field-visit agent for Dermalogica's sales reps and educators and supplier-risk monitoring for central procurement.

AI governance / Adoption / Hackathon
Portfolio SpendUp to 30% Off

One negotiation instead of eight

Replaced brand-by-brand software deals with portfolio-wide contracts, consolidating volume across brands to win discounts of up to 30%.

Vendor strategy / Contract negotiation
Predictive Logistics$4.5M a Year

Fixing late orders before they were late

At Shutterfly, built a model that flagged orders likely to ship late, so operations could act before the deadline instead of paying for last-minute shipping upgrades and refunds.

Python / Machine learning / Operations