Strategy in practice

Challenge

A statewide eligibility platform needed coordinated releases across business, engineering, QA, and operations — each valued at $800K+ — with tight regulatory timelines and 20+ stakeholders with competing priorities.

Approach

Established release sequencing by evaluating business impact, dependencies, and delivery risk. Ran structured cadences — biweekly client syncs, pre-design sessions, and design reviews — to maintain alignment and surface blockers early.

Impact

• Delivered 4 high-value releases annually, each on time and within scope
• Managed delivery for workflows affecting ~1 million consumers
• Reduced pre-production defects by 30% through improved deployment readiness

Challenge

An internal team of 100 needed a way to synthesize market intelligence faster. No existing tool fit the workflow, and leadership needed a working product — not a deck — in under 30 days.

Approach

Defined scope, milestones, and cross-functional ownership from concept through launch. Integrated voice interaction, retrieval workflows, and external intelligence APIs into a single research flow. Built onboarding materials to scale beyond the pilot.

Impact

• Shipped a working MVP in under 30 days
• Adopted by a team of 100, featured in 6-7 leadership demos
• Integrated 4 external tools and APIs into a unified experience

Challenge

A customer communications workstream was generating redundant notices across millions of records, eroding consumer trust and creating operational waste. The root cause was unclear and spanned multiple system conditions.

Approach

Led SQL-based analysis across millions of records to isolate the operational and system conditions driving duplicates. Translated findings into process enhancements and release-ready recommendations through 20+ stakeholder sessions.

Impact

• Reduced redundant customer notices by 20%
• Improved operational efficiency across the communications pipeline
• Established a repeatable analysis framework for future issue identification

Challenge

The product team needed to understand user engagement patterns, quantify the value proposition for cost savings, and evaluate pricing tier adjustments — all with limited analyst resources.

Approach

Analyzed 200K+ user records in PostgreSQL to quantify gas savings potential. Used Python to uncover engagement patterns informing rollout decisions. Modeled pricing changes in R to simulate customer behavior under different tiers.

Impact

• Uncovered 20% average gas savings ($71/user), fueling cost-saving positioning
• Drove a 30% lift in route optimization usage through data-informed rollout
• Recommended a 5% pricing adjustment tied to improved retention and revenue KPIs