Matthew McKelvy · Compensation & workforce strategy
I work on the systems behind what work is worth.
Most recently, I advised senior leaders across Cisco’s global go-to-market organization of about 25,000 people. My work sits across compensation strategy, workforce design, and the analytical systems behind hiring and pay decisions.
The range
Different problems. The same discipline: make the evidence legible, make the structure explicit, and make the decision reviewable.
one country separated from the others,
Attrition was one signal. Hiring was the other.
Both pointed to the range.
One GTM function in APJC was seeing elevated attrition in one country, and the roles left open behind it were not filling. I compared the same work across Country A, Country B, and Country C. The pattern pointed to the market reference ranges: Country A had drifted below the relevant peer median. Incumbent pay sat lower inside those ranges, while new offers were competing from the same disadvantaged position.
- SignalOne GTM function shows elevated attrition and persistent vacancies.
- CompareSame work, Country A vs. Country B vs. Country C.
- Re-plotRange midpoints against the relevant peer-market median.
- FindingCountry A’s ranges had drifted below market.
- RecommendationRecalibrate the ranges, then review incumbent positioning and hiring offers.
Question → Analysis → Recommendation
Illustrative data. The reasoning is real. Annual range cycles can lag a fast-moving labor market. When they do, the gap can surface in both retention and hiring.
titles multiply faster than the work changes.
Hundreds of role cards. One architecture.
Successive reorganizations had scattered the company-wide job catalog. AI helped read and group hundreds of role cards, the internal job descriptions. I built the structure: job family groups, job families, titles, and the roles that did not fit cleanly anywhere. Then I took the architecture to the compensation lead for every business unit for review and sign-off ahead of the Workday build.
Raw roles→ AI-assisted structuring→ Human judgment→ Architecture
Illustrative structure, not Cisco’s. Job families across, levels down (four individual contributor, two manager), each dot a job profile.
exceptions should not depend on who tells the best story.
Make the decision system visible.
Out-of-range offers were being decided case by case. I built a model that put the same evidence in front of each decision: external market data, the candidate’s position in the range, and internal peers. The version here uses a fictional candidate and public 2026 market data. The decision logic is the same.
$250,000
Proposed OTE, Sr. Solutions Engineer
Base $175,000 + target incentive $75,000 (70/30)
Level: Senior IC (Radford P4-equivalent)
Within guidelines
- Compa-ratio
- …
- OTE ÷ range midpoint, on a total-cash range, the way sales roles are usually priced
- Range penetration
- …
- position from minimum to maximum
- Market position
- …
- against the market OTE composite, interpolated to the nearest 5
- Internal peers
- …
- same role and zone
Illustrative composite modeled from public 2026 market data, with sources below. Not a survey and not any employer’s pay data. Cash only: equity, sign-on, and accelerators excluded.
The data narrows the decision. Judgment still makes it. The model makes that judgment more consistent and easier to review.
Read the organization as a system.
Before compensation, I worked on workforce strategy across Cisco’s Sales and Finance functions. Attrition, time-in-grade, grade distribution, and headcount each described a different part of the organization. Read together, they showed where structure no longer matched the work. The analysis informed org-design recommendations for both functions and the business case for Cisco’s FY2022 org design playbook.
Signals → One picture → Recommendations
Tools
The questions stayed the same.
The tools changed.
Spreadsheet→ Model→ Dashboard→ AI tooling
I build tools when they make a decision easier to see, repeat, or explain: compensation models in Excel, dashboards in Tableau, workforce analyses in Workday Adaptive Planning, and AI-assisted workflows for architecture and mapping. The tool is secondary. The standard is the same: the reasoning should be inspectable.
Five years across one very large system.
Most recently, I advised senior leaders across Cisco’s global go-to-market organization of about 25,000 people on compensation structure, market positioning, workforce planning, and resource allocation.
Worked on a global workforce strategy program across Sales and Finance. Coordinated functional owners, built leadership reporting, developed attrition, time-in-grade, and grade-distribution analyses in Workday Adaptive Planning, and translated them into org-design recommendations. Wrote the business case for Cisco’s FY2022 org design playbook.
Benchmarked Cisco’s UK compensation against peer-company data, role by role, and developed competitive-positioning recommendations for country leadership. Supported UK & Ireland strategic planning with organizational and workforce analysis.
Advised senior leaders across the go-to-market organization on compensation structure, market reference ranges, workforce planning, and resource allocation. Leveled roles, modeled complex offers, maintained incentive targets, and built the M&A mapping dashboard used to map acquired Splunk roles into Cisco’s architecture.
Excel for compensation models. Tableau for decision dashboards. Workday Adaptive Planning for workforce analysis. AI-assisted tooling where it improved speed or structure. The less visible work mattered too: budget rollups, data checks, and the operational detail that makes a recommendation usable.
Boston University, B.S. Communication · Georgetown University, graduate coursework
Outside work
Usually on a tennis or squash court, or somewhere on the Monterey Peninsula.