Building at the intersection of finance & intelligence.

A finance professional on the private equity and M&A track, bridging finance and AI — I build the tools that source, score, and pressure-test deals.

130+
PE & VC investments screened — Transal
Multi-$B
VC/PE portfolio on a two-person team with the CIO — Transal
$50M+
Annual M&A pipeline tracked — Walgreens
80%
projected SOX-testing reduction from an AI pipeline I built — Walgreens

About

A finance mind with a builder's edge.

I started my first company at fifteen — a gold-refining operation built in partnership with Genesis Electronics, one of the largest electronics-recycling facilities in Illinois. Chemistry taught me to build; finance is where I stayed. I studied finance and mathematics at the University of Miami, cum laude, then moved through high-frequency algorithmic trading, equity research, and — alongside the CIO, as the two-person team behind a multi-billion-dollar venture and private-equity portfolio. Today I'm back at Walgreens on the AI team, bridging the gap between finance and AI: building the tools that source, score, and pressure-test deals and drive real cost savings, moving from raw data to a decision faster than a traditional analyst.

Private Equity M&A AI Systems Financial Modeling Deal Origination

Deal Work

How I think about a deal.

A sample investment memo — the same sourcing engine from my projects, turned into a thesis, a valuation, and a recommendation.

Investment Memo · IllustrativePublic NCPA / CMS data + proprietary screening

Independent Pharmacy Roll-Up — Acquisition Thesis

Buy-and-build consolidation of independent community pharmacies, sourced via a proprietary AI screening engine I built.

Recommendation

Pursue a buy-and-build roll-up of independent community pharmacies, using the AI screening engine to acquire owner-operated stores off-market at low-single-digit EBITDA multiples, integrate them onto a shared purchasing and clinical platform, and exit a scaled regional operator at a higher multiple. A focused 25–35-store platform is achievable in 4–5 years; the edge is proprietary deal flow, not capital. The return hinges on exit-multiple expansion, which current retail-pharmacy comps work against, so the honest base case is ~2.0× MOIC at a modest ~5× exit, with downside near capital-preserving if multiples stay compressed.

Market Opportunity

~19,000 independent community pharmacies (NCPA 2025) generate ~$103B in annual revenue — a fragmented, owner-operated market consolidating at more than one closure a day. Owners are operators, not financial sellers, so processes are uncompetitive and pricing is negotiable. Fragmentation plus a retirement wave is the classic roll-up setup.

The Sourcing Edge

My platform scores a ~65k-record universe of non-chain pharmacies on a 6-factor model. I rejected normalization methods that inflated the target list to 25k–50k stores and kept a deliberately conservative score — isolating just 820 high-conviction targets, with the top 100 holding 83–85% stable under ±10% stress. The discipline is the moat: a short, vetted, off-market pipeline.

Target Profile

The 820 targets share a clear signature versus the field: 86% show stale licensing records (a retirement / disengagement proxy) and serve higher-income ZIPs ($98k vs $66k median), a profile that reads more suburban than rural. [[TODO: confirm the local-competition metric; the prior "2.6 vs 14.9 per 10k" figure looks off, since ~2.6/10k is close to the U.S. average and should not be cited as low-competition evidence.]]

Valuation Framework

Two reference points, not cross-checks; they can differ 3–5×. A file buy at ~$3–5 per annual script sets a downside floor (chains pay $5–12+ in competitive deals). A whole-business, going-concern buy at ~2.5–4.0× EBITDA plus inventory is the operating price we actually underwrite. Independents run ~22% gross margin (NCPA), so entry margins are thin with room to expand. Base entry: ~3.5× EBITDA.

Illustrative Exit-Multiple Sensitivity

Exit multipleScenarioMOIC
~3.5× (flat, no expansion)Downside~1.0–1.3×
~5.0× (modest)Base~2.0×
~7.5× (current comps)Upside~3.0×

Holds a ~30-store, ~6%-margin build at ~3.5× entry constant and varies only the exit. The return is driven by multiple expansion, so exit is the swing factor and the primary risk: retail-pharmacy comps have compressed (Rite Aid's bankruptcy, Walgreens taken private, CVS under margin pressure). A true downside that also pushes margin below entry (~3–4%) and exit toward ~4× lands near ~1.0× MOIC or below. Pace check: 820 scored targets at a ~6% win rate is ~49 reachable, so a 30-store build is disciplined, not heroic.

Value-Creation Plan

  • Purchasing scale: modest GPO / wholesaler gains on a ~78% COGS base, haircut because buying groups already capture most independent GPO pricing (~+50–75 bps, illustrative)
  • Central fill & shared back office: ~$50–80k saved per store, the most reliable lever
  • DIR-fee timing & appeals: treated as timing and recovery only, not a structural margin lever; PBM / DIR economics are Risk #1 below, not a lever
  • Net target: illustrative lift from ~4% toward ~5% of revenue, contingent on the above [[TODO: confirm net margin target]]

Structure & Pacing

  • Leverage: [[TODO: confirm assumed acquisition leverage / debt structure]]; if SBA 7(a), each loan caps at $5M
  • Deployment: gradual, roughly 6–8 stores per year over 4–5 years, not a single close
  • Platform G&A: central-fill, compliance, and integration overhead [[TODO: confirm platform overhead]]

Key Risks & Mitigants

  • PBM & DIR pressure (CMS 2024 reforms; state PBM-delinking laws) → diversify into cash-pay clinical services
  • Generic deflation → purchasing scale plus a richer service mix
  • Amazon / mail-order → focus on high-touch local and rural markets
  • 340B is upside only, not core EBITDA — given the 2024 appellate ruling on manufacturer restrictions and HRSA's 2026 rebate-model pilot
  • Integration & pharmacist retention → earnouts, retention packages, phased onboarding (base case assumes modest post-close attrition)

Assumptions & Limitations

Illustrative case study — not a live deal or a forecast. Market context from the NCPA 2024–2025 Digest, CMS, and Drug Channels; valuation and return assumptions reflect 2024–2026 comparables and are clearly bounded. Target counts and screening signals are outputs of my proprietary, un-audited model. Returns are scenario estimates, highly sensitive to store count, margin capture, and exit multiple.

Selected Work

Tools built where capital meets code.

Three production platforms I built to do deal work better — SMB tax intelligence, pharmacy M&A, and multi-vertical deal origination. Proof of the edge, not the day job.

Fig. 01 — SMB Health & Tax Strategy DashboardActive Development
01
Active Development

Bearing

A merged SMB consulting dashboard combining a full business-health analysis platform with 226 tax-saving strategies. Connects to QuickBooks for auto-fill, profiles each business across financial health, operational efficiency, and growth readiness, then matches them to applicable tax strategies ranked by estimated savings.

The Problem

Small business owners don't know which tax strategies apply to them, and consultants waste hours profiling each client manually.

The Solution

An integrated platform that pulls real financial data from QuickBooks, runs multi-dimensional business-health analysis, and matches the profile against 226 tax strategies — each backed by IRC sections, Treasury Regulations, and court opinions — surfacing the highest-value opportunities first.

How It Works

i.

Business Health Analysis

Financial health, operational efficiency, growth readiness; QuickBooks auto-fill of live P&L, balance sheet, cash flow.

ii.

226 Tax Strategy Engine

Each backed by IRC sections, Treasury Regs, court opinions; fact-dependency decision trees; savings estimation; priority ranking by estimated savings.

iii.

Integrated Dashboard

Client profiles with health scores; auto-matched strategies with eligibility explanations; consultant workflow intake-to-delivery; QuickBooks OAuth.

Highlights

  • 226 tax strategies backed by primary legal authority
  • QuickBooks integration
  • Multi-dimensional health scoring across 3 domains
  • Fact-dependency decision trees
  • Estimated dollar savings per strategy

Technology

PythonStreamlitQuickBooks APISQLiteTax Knowledge BaseDecision Trees

Experience

A track record across the deal lifecycle.

From high-frequency trading to a multi-billion-dollar venture and private-equity portfolio to bridging finance and AI.

Jan 2026 – PresentChicago, IL

AI/ML Finance Analyst

Walgreens — AI Team
  • On Walgreens' internal AI team, bridging finance and AI — turning finance, M&A, and controls workflows into automated, model-driven systems.
  • Built the M&A deal-tracking dashboard and scorecards behind $50M+ in annual capital deployment — evaluating strategic fit, synergy case, and EV/EBITDA against precedent transactions.
  • Leading a cost-saving AI automation initiative projected to replace ~80% of SOX controls-testing hours via Python-driven exception detection on audit logs.
  • Built an executive analytics platform consolidating management reporting across four finance functions, cutting manual data prep ~60%.
Finance × AIM&A IntelligencePythonAI AutomationExecutive Analytics
Aug 2025 – Dec 2025Miami, FL

Investment Analyst

Transal Corp · Multi-Billion-Dollar Family Office
  • One half of a two-person investment team alongside the CIO — direct ownership of sourcing, diligence, and monitoring across a multi-billion-dollar portfolio.
  • Underwrote 130+ sponsor-led private-equity commitments and co-investments across buyout, growth equity, and private credit; produced IC-ready memos covering return profile, leverage, and downside cases.
  • Built IRR, MOIC, DPI, and TVPI attribution models benchmarking 40+ GP track records against Cambridge Associates quartiles to inform quarterly re-up and pacing decisions.
  • Developed a five-year capital-call and distribution pacing model across the portfolio, surfacing a funding gap that reshaped the 2026 commitment schedule.
  • Used Addepar for portfolio oversight, exposure monitoring, and co-investment screening across 130+ fund positions.
Private EquityGrowth EquityPrivate CreditCo-InvestmentsFund Modeling
Jun 2025 – Aug 2025Chicago, IL

Finance Intern

Walgreens — Specialty Pharmacy
  • Engineered a SQL + Power BI profitability pipeline over millions of quarterly prescription records, surfacing per-script margin drivers across a multi-hundred-million-dollar specialty-drug portfolio.
  • Delivered ad hoc drug-level financial insights to therapy directors and field leadership.
  • Developed a dietary-supplement vending-machine concept using DCF modeling and private-credit financing.
SQLPower BIData AnalyticsDCF Modeling
May 2024 – Oct 2024Miami, FL

Equity Analyst Intern

Maredin Wealth Advisors
  • Authored a long thesis on Snowflake (NYSE: SNOW) — three-statement model, DCF, and bull / base / bear sensitivity — covering platform economics and consumption pricing.
  • Defended the recommendation directly to the RIA's founder.
Equity ResearchThree-StatementDCFInvestment Analysis
Jul 2023 – Feb 2024Chicago, IL

Algorithmic Trading Intern

Core Value Capital LLC
  • Designed and backtested a high-frequency mean-reversion FX strategy across 13 currency pairs with walk-forward cross-validation in Python.
  • Improved risk-adjusted returns by pruning 15% of pairs flagged for leverage tail risk.
High-Frequency TradingAlgorithmic TradingPythonQuantitative AnalysisRisk Management

Skills & Credentials

The toolkit behind the work.

01

Financial Modeling

  • LBO Modeling
  • DCF Analysis
  • M&A Modeling
  • Accretion / Dilution
  • Comparable Analysis
  • Precedent Transactions
  • Three Statement Models
  • IRR / MOIC
  • DPI / TVPI
  • Capital Call Pacing
  • Mathematical Modeling
02

Technical Skills

  • Microsoft Excel (Expert)
  • VBA Macro Development
  • Power BI
  • SQL
  • Python
  • Addepar
  • Bloomberg Terminal
03

AI & Automation

  • Claude Code
  • Claude Opus & Sonnet
  • Anthropic & OpenAI SDKs
  • Model Context Protocol (MCP)
  • Cursor
  • GitHub Copilot
  • OpenRouter
  • Local LLMs (Ollama · Qwen · Gemma)
  • RAG Pipelines
  • Vector Search & Embeddings
  • Agentic Workflows
  • Multi-Agent Orchestration
  • Tool Calling & Structured Outputs
  • Prompt Engineering & Evals
  • AI Data Extraction & NLP
  • LLM Workflow Automation

Education

University of Miami

Miami Herbert Business School — B.B.A. Finance, Minor in Mathematics · Cum Laude · GPA 3.79

Provost's Honor RollDean's List

Certification

Wall Street Prep (WSP)

Financial Modeling Certification

LBODCFM&ACompsPrecedentsThree StatementExcel VBA

What Drives Me

Three convictions, one direction.

01

Artificial Intelligence

AI will fundamentally reshape finance — from deal sourcing and due diligence to portfolio construction and risk management. Actively building tools and workflows that put AI at the center of the investment process.

02

Buying Companies

Finding an undervalued business, structuring a deal, and creating value through operational improvement and strategic growth. M&A and acquisitions are where I want to build my career.

03

Private Markets

PE, VC, private credit, co-investments — the corners of the market where analysis actually moves the outcome. My edge is the tooling: I build the systems that source and pressure-test deals faster than a traditional analyst, so I can screen more and conviction-check harder before capital is committed.

Contact

Let's connect.

I'm always interested in discussing deals, AI applications in finance, or new opportunities.

Download résumé (PDF) ↗