The finance system behind M&A, FP&A, payor strategy, and post-close execution.
Director of Finance at a PE-backed healthcare platform. I own the model, present it to the sponsor, and run the cadence that holds the value-creation plan together. Built for sponsors who want a finance leader who can do both.
OPERATOR
SINCE 2019
Finance leadership scope.
Each case below is real work I owned and presented to a sponsor or board. The workbooks shown here are reconstructed with sanitized assumptions for confidentiality. Pull one, then book a call and we'll go through the assumptions, the sensitivities, and what I'd own differently today.
Operating cadence.
The case studies show outcomes. This is the rhythm underneath them. The cadence that turns deal models, payor strategy, and FP&A into a finance function a sponsor can actually run a business on.
Sample board artifacts.
Two artifacts I run every month. The EBITDA bridge is what the sponsor opens first. The value creation tracker is what we work against between board meetings. Numbers and initiative names are illustrative; structure and methodology are real.
| Initiative | Baseline | Target | Impact | Owner | Status |
|---|---|---|---|---|---|
| Payor renegotiation | NCR 88% | NCR 91% | +$1.1M | DoF | Realized |
| AR cleanup | 52 days | 38 days | +$0.4M cash | RCM · DoF | In flight |
| Tuck-in · State X | n/a | $0.4M syn. | +$0.4M | DoF · COO | On plan |
| Provider productivity | 4.5 wRVU/d | 5.2 wRVU/d | +$0.3M | COO · DoF | Realized |
| FP&A automation | Manual | Daily auto | +0.1 FTE | DoF | Realized |
| Denial reduction | 9.2% | 6.0% | +$0.2M | RCM | At risk |
Operating metrics I watch.
The six numbers a healthcare finance leader has to read fluently. Anyone in this seat should be able to define them, defend them, and know what action each one drives. This is the vocabulary every operating conversation I'm in actually uses.
The body of work.
How I owned the payor renegotiation that drove a 20% top-line lift.
- The problem
- Multi-clinic platform with manual reporting that obscured payor-level economics. Renegotiation conversations were anecdotal, not analytical.
- What I owned
- Built the payor-mix and rate-sensitivity analysis end-to-end. Modeled contribution by site, by payor, by procedure. Presented the renegotiation case to leadership and anchored the strategy on the contracts that mattered.
- The outcome
- Renegotiation contributed to a 20% top-line revenue lift. Reporting cycle time cut ~40% along the way.
How I led diligence and stood up Day-1 reporting the morning a tuck-in closed.
- The problem
- Multi-site specialty platform pursuing tuck-ins. Diligence had to move fast and clean. Covenant tracking, ASC 805 fair-value, and integration risk were all gating close.
- What I owned
- The full deal underwriting end-to-end: Sources & Uses, purchase price waterfall, FVA step-ups, accretion/dilution, synergy build. Managed the QoE with Transaction Advisory and led the integration finance plan.
- The outcome
- Day-1 reporting, controls, and covenant tracking live the morning of close. Sponsor walked into the first board meeting with the numbers already running.
How I standardized diligence across a 100-location PE rollup and cut close time 20%.
- The problem
- PE-backed multi-state specialty platform. Acquisition pace required standardized diligence and a defensible returns model on every target.
- What I owned
- The LBO and diligence framework applied to every target: capital structure, debt paydown, IRR/MOIC sensitivity, integration assumptions. Built the performance dashboards in Python and SQL that the operating team ran on.
- The outcome
- Transaction closing time reduced 20%. Network scaled to 130+ physicians, 100+ locations, 11 states on the same playbook.
What I learned about systems and leadership from inside the Fortune 500.
- The situation
- Two Fortune 500 environments early in my career. A Fortune 50 telecom serving 145M+ wireless customers. A national dialysis network of 2,600+ centers and 190,000+ patients. Scale changes how you build reporting, run decisions, and structure span of control.
- What I picked up
- Operating-system thinking. Reporting cadences that hold up across thousands of operating units. Standardized analytics so a regional VP sees the same number the CFO sees. A healthy respect for which decisions get owned at the edge and which get owned at the center.
- What carried over
- The same systems mindset now applied to every PE-backed platform I work on. Standardize the spine; localize at the edges. Run reporting that breaks predictably, not silently.
Credentials.
Verified by a 4-hour proctored build exam. Shipped an embedding-based retrieval chatbot with OpenAI structured outputs and conversation history.
The full domain layer underneath every healthcare AI workflow I scope. What AI actually changes for health systems, where it bends to equity and disparities, and where it still has to earn trust before it deploys.
Multi-course IBM program on agentic AI: agent design patterns, tool use, orchestration, memory, and the evaluation discipline that keeps an agent honest in production.
The full Excel modeling discipline. Built a Markowitz portfolio that beat single-stock AAPL on real 2016 data, with the workbook and the deck to defend it.
Six-course program: valuation, deal structuring, tax mechanics, accretion/dilution, IPO process. The vocabulary every M&A diligence conversation runs on.
Frameworks for leading with technology and AI, and making the strategic call when the right answer isn't obvious.
The base layer of quantitative thinking. When deterministic, probabilistic, and optimization are the right tool, and when each is the wrong one.
Production-grade data science foundation: Python, statistics, ML pipelines, and the discipline to ship a reproducible analysis.
Asset-class fluency: equities, fixed income, derivatives, currencies. The working vocabulary every valuation conversation runs on.
Finance automation I own.
One live CRE product, a flagship M&A underwriting agent, and a ten-lab agentic AI portfolio targeting the mechanical, high-stakes work corporate finance leaders actually own: close, LBO, payor analytics, covenant compliance, 13-week cash, board deck, BvA, diligence, PPA. Every lab runs end-to-end on sample data with zero LLM keys required.
CRE Underwriting AI
ShippedA CRE underwriting automation tool I architected end-to-end. AI rent-roll extraction, NOI / cap-rate / IRR engine, sponsor-grade reporting. Walkthrough on request.
M&A Underwriter
Live demoCIM PDF in → full LBO model + IC memo out. Sources & uses, 5-yr projection, debt waterfall, IRR/MOIC, sensitivity, accretion/dilution. End-to-end agentic deal underwriting.
Finance AI Labs
IndexThe full ten-lab portfolio: agentic AI projects covering close, LBO, payor analytics, covenant compliance, 13-week cash, board deck, BvA, diligence, PPA. Each runs without an API key.
Payor Analytics Agent
Active835/837 in → payor mix waterfall, denial root cause (CARC clustering), contracted-rate variance, AR aging by payor. The healthcare RCM lab.
Covenant Watch
ActiveCredit agreement in → covenant grid + forward compliance forecast. Max leverage, min DSCR, min liquidity, fixed-charge coverage. Breach forecasting before the lender call.
13-Week Cash
ActiveAR + AP + payroll in → rolling 13-week cash forecast across base / stretch / stress scenarios. The treasury lab every PE-backed CFO eventually owns.
GL Autopilot
ActiveBank + card feed in → GL-coded transactions with vendor-learned rules, duplicate detection, MAD-based anomaly flags. The close-acceleration lab.
Earnings Intel
Active10-K/Q + transcript feed in → peer comp pack with QoQ/YoY deltas, normalized KPI grid, narrative call-outs. Strategic-finance briefing on autopilot.
Board Deck Agent
ActiveKPI snapshot in → director-grade slide plan with walks, highlights, and risk slides. The QBR-prep lab.
BvA Narrator
ActiveBudget vs actual in → volume/rate/mix decomposition with materiality filter and severity flags. The FP&A close-narrative lab.
Diligence Q&A
ActiveVDR documents in → cited buyer Q&A answers with TF-IDF retrieval, redaction guard, and source snippets. Sell-side diligence at the speed of email.
PPA Allocator
ActivePost-close inputs in → fair-value step-ups, intangible amortization schedule, goodwill residual, ASC-805-cited memo, opening balance sheet. Audit-ready.
The arc.
Where I fit.
The seat I'm built for
- PE-backed lower- to mid-market healthcare. Multi-site, multi-state, payor-exposed.
- Director of Finance → VP Finance → CFO track. Hands-on enough to own the model. Senior enough to present it to the sponsor.
- Operating cadence + M&A engine. Standing FP&A rhythm and 2–6 tuck-ins a year.
- Sponsors who want a finance leader who runs the cadence and owns the analysis. Not one or the other.
What I'm not
- Not a fund-side investment analyst. I run portfolio finance, not sponsor diligence.
- Not a Big-4 controller. I lean on accounting partners for technical close work; I own the analytics and decision support around it.
- Not a public-company controller. SOX, 10-Q rhythm, and Street-facing reporting are not where I've operated.
- Not a generalist consultant. I work inside one platform, accountable for the same numbers month after month.
Open the model.
Then let's talk.
Twenty minutes. I'll walk through the assumptions, the sensitivities, and the parts I'd own differently if I were doing it today.
Pick a time. I'll walk you through the model.
Twenty minutes. Open the workbook with me. Ask the hard questions about the assumptions.
I'll send it straight to your inbox.
Let's talk.
Twenty minutes. Drop your info and I'll reach out to confirm a time.