Selected Work

Examples of how I’ve worked through complex, cross-team problems in real environments.

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Context

A complex initiative was already in production with executive visibility and customer impact. What started as a proof-of-concept had moved forward before operational discipline was fully in place.

Multiple teams were involved across product, engineering, and data science—each contributing to delivery, but without a shared structure to coordinate the work.

 

What Was Breaking Down
  • Timelines were committed without validating feasibility
  • Reporting didn’t reflect actual delivery risk
  • Cross-team dependencies weren’t aligned
  • Production exposure outpaced testing rigor
  • Teams were burning out and trust was declining
What I Did
  • Introduced shared, objective delivery reporting
  • Separated experimental and production workstreams
  • Established data-readiness checks before committing to scope
  • Rebuilt planning and escalation mechanisms
  • Clarified ownership and decision pathways across teams
What Changed
  • Risks were identified earlier, reducing reactive escalations
  • Production stabilized while allowing work to continue moving forward
  • Forecasting became more reliable across teams
  • Confidence in delivery improved at both team and leadership levels
Context

A product organization was growing quickly—adding teams and expanding scope faster than its operating rhythms evolved. Work was moving, but coordination and planning hadn’t kept pace.

As complexity increased, execution became more reactive and release predictability started to slip.

 

What Was Breaking Down
  • Dependencies across teams were tracked informally
  • Roadmap commitments weren’t grounded in actual capacity
  • Leadership didn’t have clear visibility into delivery health
  • Releases slipped as coordination gaps surfaced late
What I Did
  • Introduced structured dependency mapping across teams
  • Aligned roadmap sequencing with actual team capacity
  • Implemented lightweight, consistent reporting cadences
  • Established clearer tradeoff discussions before commitments
What Changed
  • Release slippage and last-minute scope changes decreased
  • Quarterly planning became more reliable
  • Leadership gained clearer visibility into delivery risk
  • Teams shifted from reactive work to more structured execution
Context

A manufacturing environment needed a better way for factory floor workers to access operational guidance and troubleshooting information during production.

The idea was to build an AI assistant that could surface relevant knowledge in real time—but making that work reliably required more than just a model. It required coordination across data, product, engineering, and user experience.

 

What Was Breaking Down
  • Operational knowledge wasn’t easily accessible during active workflows
  • No clear source of truth for guidance the system could rely on
  • Data wasn’t structured or maintained in a way that supported reliable outputs
  • The experience needed to fit into real-world usage, not interrupt it
What I Did
  • Brought design into the work early to align the experience with real factory workflows
  • Partnered with domain experts to identify and structure reliable operational knowledge
  • Established processes to maintain and update the underlying data over time
  • Coordinated across product, engineering, and data teams to move from concept to production
  • Aligned the work with broader product efforts to support adoption and future expansion
What Changed
  • Workers were able to access relevant guidance directly within their workflows
  • Operational knowledge became more structured and usable across the system
  • The assistant moved from concept to a usable production tool
  • Foundations were established to support future AI-driven capabilities

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