AI-focused product and UX leader with 15+ years turning real-world behavior into workflows, systems, and launched features across consumer products, edtech, SaaS, and AI. I specialize in 0→1 product thinking, human-in-the-loop systems, agentic workflows, trust and safety, evaluation design, and the hard product decisions required to move AI from capability to real adoption.
years across product design, research, UX, startups, edtech, AI, SaaS, and gaming.
countries reached through multilingual literacy product work.
literacy outcome improvement measured through longitudinal work.
reduction in reviewer burden through AI-assisted moderation workflow design.
new-user growth after shipping SimplifyEm’s forms feature.
I build AI-enabled products the way strong teams actually need them built: define the workflow, identify failure modes, specify guardrails, align cross-functional partners, and ship against measurable outcomes—not hype.
My career has always centered on how people interpret systems, where they lose trust, and what they do when products fail to match real behavior. That is the core of AI product management: turning ambiguous human workflows into systems that are useful, safe, evaluable, and ready for production.
I make roadmap calls when the signal is messy: stop full automation when false positives are too costly, choose offline-first when field conditions demand it, and prioritize workarounds when users have already written the spec with their behavior.
I define release gates around quality, safety, reviewer agreement, escalation reasons, override rates, and real user outcomes, not vanity demos or vague claims that the model is “better.”
I know where humans belong in the workflow: high-risk decisions, low-confidence model output, edge cases, cultural context, and irreversible actions. The goal is not automation at all costs. The goal is accountable scale.
I can move from discovery to PRD to prototype to launch because I have sat in the research, design, engineering, executive, startup, and operations seats. I do not need translation between functions. I am the translation layer.
These projects show the pattern: find the real user behavior, define the system, make the tradeoff, ship the intervention, and measure whether it actually changed the outcome.
I led end-to-end product development for an AI-assisted moderation workflow where automated systems were making confident wrong calls on culturally specific harm signals. I reframed the roadmap away from full automation and toward a hybrid system that routed uncertainty to human expertise.
At Learning Ovations, I led product and UX thinking for A2i, an Assessment-to-Instruction system that combines student literacy assessments, data-driven instructional recommendations, lesson-planning support, and professional development to help teachers individualize reading instruction. After Scholastic acquired Learning Ovations, I continued that work inside a broader literacy platform, focusing on how educators onboard, interpret recommendations, and turn assessment signal into classroom action.
Players with large friend lists were already hacking the product with symbols in names to identify close friends. I treated that behavior as a product requirement, not a curiosity, then designed a zero-learning-curve feature around an interaction players already understood.
I built an end-to-end AI content pipeline for independent creators from research to revenue: demand analysis, workflow architecture, LLM-assisted creation, human review gates, marketplace publishing, and performance iteration.
I want the hard interview loop: ambiguous product strategy, AI failure modes, measurement design, stakeholder conflict, and launch tradeoffs. That is where I am strongest.
I translate fuzzy user needs into acceptance criteria, eval rubrics, quality thresholds, guardrails, and launch metrics.
I find the incentives, rituals, workarounds, trust gaps, and behavioral loops that determine whether a product lives or dies.
I am comfortable discussing thresholds, review queues, labeling strategy, RAG, prompt behavior, model limits, edge cases, and telemetry.
I will challenge the popular solution when the evidence points elsewhere, then align the room around the decision and the metric.
My style is direct, evidence-led, and outcome-obsessed. I am warm with people and ruthless with weak product logic.
When users invent parallel systems, the roadmap is already speaking. The PM’s job is to listen before the behavior calcifies into churn.
The best AI products know when to automate, when to route, when to explain, and when a human must own the final decision.
I build measurement into the spec because post-launch learning should not depend on dashboards someone remembered to add later.
The arc matters: language and meaning, product design, scaled UX, founder-level ownership, and now AI systems where trust and interpretation are the product.
Built AI personas and experiments using custom model workflows, image generation, and voice synthesis to study trust, disclosure, engagement, and safety dynamics in AI content.
Advise on AI adoption, trust research, human behavior in AI systems, and how organizations should evaluate what happens to users after AI features ship.
Built an early-stage food-tech startup from zero: brand, product, hiring, team operations, investor materials, governance, and hard leadership decisions through company dissolution.
Designed digital reading products, translated research into product decisions, and shipped features across the design-to-delivery pipeline for a global children’s publisher.
Led UX for edtech reading intervention tools, combining research, wireframes, prototypes, and cross-functional collaboration for struggling readers and educators.
Designed Favorite Friends end to end across research, personas, prototypes, and UI specs for a product used at global consumer scale.
Led product design and QA for property management software; shipped a forms feature that increased daily new users from 25 to 400.
Studied language, semantics, literary criticism, and meaning-making, then taught English literature and founded education and public health community initiatives.
JTBD, ethnography, usability testing, PRDs, roadmap planning, AI evaluation, HITL workflows, RAG knowledge systems, prompt behavior, trust and safety, Figma, and executive communication.
I’m looking for AI product roles focused on 0→1 products, enterprise and professional workflows, human-in-the-loop systems, agentic experiences, evaluation frameworks, trust and safety, and cross-functional execution from discovery through launch.