Matthew McKelvy · Compensation & workforce strategy
I work on WHAT WORK IS WORTH.
Compensation and workforce strategy, most recently as a compensation analyst advising senior leaders across Cisco’s go-to-market organization of about 25,000 people. I came for the spreadsheets and stayed for the questions: every pay decision turns out to be a question about how the organization really works, and I like finding out.
The range
Different problems, one habit: get the evidence right, put it in a structure someone else can check, then make the call. In that order, on the good days.
nobody resigns over a midpoint,
ATTRITION WAS THE SIGNAL.
TRACED TO THE RANGE.
One region kept losing people, across go-to-market, operations, and engineering at once, and the open roles behind them were not filling. The market reference ranges for those roles had quietly drifted below the peer median. Pay and offers both live inside the range, so the people had drifted below market and the offers were losing to it.
- SignalOne region separates from the others.
- PatternGo-to-market, operations, and engineering, all in one geography.
- Re-plotRange midpoints against the peer median.
- FindingThe ranges had drifted below the peer median.
- RecommendationRecalibrate the ranges, then review the people inside them.
QUESTION → ANALYSIS → RECOMMENDATION
Illustrative data. The reasoning is real. Structure adjustments move ranges on an annual cycle, a fast-moving labor market outruns them, and the gap compounds.
titles multiply faster than jobs do,
HUNDREDS OF TITLES. ONE ARCHITECTURE.
Successive reorganizations had scattered the go-to-market job catalog. AI did the reading across hundreds of role cards, the internal job descriptions. I did the structuring: job family groups, job families, and titles, plus the roles that refused to fit anywhere, then took it to the compensation lead in every business unit for 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.
every exception arrives with an excellent story,
BUILD THE SYSTEM.
Out-of-range offers were being argued case by case. This is modeled on the tool I built so the same question got the same treatment every time: external market data, the candidate’s position in the range, and internal peers in one view, with a fictional candidate and public 2026 market data. Pick a role, drag the number, and argue with it.
$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 just makes the judgment easier to review, and harder to make two different ways.
WHAT IS THE ORGANIZATION TELLING US?
Before compensation, I worked on the organization itself: a global workforce strategy program across Cisco’s Sales and Finance functions. Attrition says who is leaving. Time-in-grade says who is waiting. Grade distribution says whether the shape of the organization matches its work. Read together, they became org design recommendations for both functions, and the business case for Cisco’s FY2022 org design playbook, the guide leaders would use the next time they restructured.
SIGNALS → ONE PICTURE → RECOMMENDATIONS
The tools
the questions stayed the same.
THE TOOLS GOT BETTER.
SPREADSHEET→ MODEL→ DASHBOARD→ AI TOOLING
The offer exception model, the M&A mapping dashboard, the AI-assisted architecture refresh: each one built for a specific decision, and only as complicated as that decision required. I enjoy building them more than a compensation analyst probably should.
FIVE YEARS INSIDE ONE VERY LARGE SYSTEM.
Most recently a compensation analyst advising senior leaders across a global go-to-market organization of about 25,000 people.
Ran a global workforce strategy program across Sales and Finance: coordinated the functional owners, built the leadership reporting, and shaped the org design recommendations for both. Built the attrition, time-in-grade, and grade-distribution analyses behind them in Workday Adaptive Planning, presented the findings to leadership company-wide, and wrote the business case for the FY2022 org design playbook.
Benchmarked Cisco’s UK pay against peer-company data, role by role, and took the competitive-positioning recommendations to country leadership. Ran the organizational and workforce analyses behind UK & Ireland strategic planning.
Advised senior leaders across the go-to-market organization on compensation structure, workforce planning, and resource allocation. Leveled roles, modeled complex offers, maintained market reference ranges and incentive targets, and built the M&A mapping dashboard leaders used to map acquired Splunk roles into Cisco’s architecture.
Compensation models in Excel, dashboards in Tableau, workforce analyses in Workday Adaptive Planning, and lately AI-assisted tool building. Also the unglamorous parts, like compensation budget rollups at the VP and SVP level, done carefully.
Boston University, B.S. Communication · Georgetown University, graduate coursework
Off the clock
STILL COMPETING.
Usually somewhere between a squash court, a tennis court, and the Monterey Peninsula. Occasionally winning.