Experience

Product leadership across AI, SaaS, data platforms, and commercial systems.

A concise view of recurring product themes in my work: turning business problems, platform constraints, workflows, and quality requirements into shipped products and measurable outcomes.

AI and workflow products

  • AI product strategy, roadmap shaping, and MVP planning for workflow-heavy products.
  • Contact Center AI delivery as one example of applying AI inside real operational processes.
  • Evaluation, quality, human review, and runtime monitoring treated as product requirements.

SaaS and data platforms

  • Gas monitoring SaaS and dashboard work focused on operational visibility and decision support.
  • Data-heavy product experiences where adoption depends on clarity, trust, and repeatable workflows.
  • Platform thinking across users, permissions, metrics, and long-term maintainability.

Monetization and marketplace growth

  • Direct Carrier Billing product work with revenue impact and cross-functional execution.
  • VOD, platform, and product growth work across customer experience, distribution, and commercial goals.
  • Ads monetization growth with attention to product mechanics, measurement, and business outcomes.

Product execution and alignment

  • Turning ambiguous business problems into shipped product increments and measurable outcomes.
  • Working across engineering, design, commercial, operations, and leadership stakeholders.
  • Balancing strategy, delivery, quality, and adoption instead of optimizing only for demos.

Areas of product depth

AI product strategy

Turning ambiguous AI opportunities into scoped product bets, evaluation plans, and launch paths.

SaaS and data platforms

Designing durable product systems with clear workflows, roles, metrics, and operating models.

Dashboards and visibility

Building dashboards, decision layers, and operating views that make product and business health legible.

Monetization and growth

Connecting product strategy to pricing, packaging, adoption, retention, and commercial outcomes.

AI-enabled workflows

Applying AI where workflows, users, risk, quality, and operational value have to work together.

AI evaluation

Treating quality, safety, edge cases, human review, and runtime signals as product requirements.

Enterprise AI delivery

Bringing strategy, stakeholders, quality, governance, and rollout planning into production delivery.

Execution

Moving from strategy to shipped product through prioritization, alignment, and measurable delivery.