Investment Analyst · Private Equity & M&A · Chicago

Joseph McDevitt

I started my first company at fifteen: a gold-refining operation with Genesis Electronics, one of the largest electronics-recycling facilities in Illinois. Chemistry taught me how to build; finance is where I stayed. I earned a Bachelor of Science in Business Administration in Finance, Minor in Mathematics, cum laude, from the University of Miami, then worked in high-frequency trading, equity research, and a family-office investment seat. Today I'm at Walgreens combining finance and AI, and I build production platforms on my own time: pharmacy M&A, small-business tax strategy, and multi-vertical deal sourcing.

Résumé (PDF) View Work Contact
130+
PE & VC opportunities screened · Transal
3
Production platforms built and shipped
Cum Laude
B.S. Business Admin, Finance · Univ. of Miami
Selected Work

Two platforms, built to do deal work better.

Pharmacy M&A Intelligence Platform

Sole builder: data pipeline, scoring model, dashboard

A full-stack acquisition-targeting platform that scores 112,000+ U.S. pharmacies for buyout readiness, built independently and applied to support real M&A discussions at Walgreens.

Stack   Python · FastAPI · React · PostgreSQL · Docker
Result

112,000+ pharmacies classified and scored across all 50 states, with an 8-stage deal pipeline tracker used to move targets from screen to close.

Fig. 01: Acquisition Targeting DashboardLive Platform
  • i.

    Registry ingestion

    Downloads and processes the full CMS NPPES registry, 112,000+ pharmacies across all 50 states/territories.

  • ii.

    Chain classification

    Pattern matching plus multi-location clustering separates independents from chains: 65,279 independents identified.

  • iii.

    Medicare & 340B enrichment

    Part D claims and 340B eligibility signals feed acquisition pricing.

  • iv.

    Census demographic scoring

    ZIP-level income, age, growth, and competition density at 99.8% coverage.

  • v.

    6-factor acquisition score

    Composite 0–100 across volume, competition, demographics, retirement risk, income, growth.

  • vi.

    Closing signals

    Stale-record and retirement-risk detection with full change history.

Python  ·  FastAPI  ·  React  ·  PostgreSQL  ·  Docker  ·  SQLAlchemy  ·  Tailwind CSS  ·  Leaflet Maps  ·  Pandas  ·  CMS/NPI Data  ·  Medicare Part D  ·  U.S. Census API
Deal Work

How I think about a deal.

Investment Memo · IllustrativePublic NCPA / CMS data + proprietary screening

Independent Pharmacy Roll-Up: Acquisition Thesis

Buy-and-build consolidation of independent community pharmacies, sourced via my own proprietary screening engine.

Recommendation

Pursue a buy-and-build roll-up of independent community pharmacies, using the 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 honest base case is ~2.0× MOIC at a modest ~5× exit, with downside near capital-preserving if multiples stay compressed.

The Sourcing Edge

My platform scores a universe of non-chain pharmacies on a 6-factor model, isolating 820 high-conviction targets out of a much larger raw candidate pool; the top 100 hold 83–85% stable under ±10% stress. The 820 share a clear signature: 86% show stale licensing records, a retirement proxy, and serve higher-income ZIPs ($98k vs $66k median) in below-average-competition markets. The discipline is the moat: a short, vetted, off-market pipeline, not a mailing list.

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×
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.

Valuation Framework

A file buy at ~$3–5 per annual script sets a downside floor. A whole-business, going-concern buy at ~2.5–4.0× EBITDA plus inventory is the price actually paid. Independents run ~22% gross margin (NCPA), so entry margins are thin with room to expand. Base entry: ~3.5× EBITDA.

Value-Creation Plan

  • Purchasing scale: modest GPO/wholesaler gains on a ~78% COGS base (~+50–75 bps, illustrative)
  • Central fill & shared back office: ~$50–80k saved per store, the most reliable lever
  • Net target: illustrative lift from ~4% toward ~5% of revenue, driven by the levers above

Structure & Pacing

  • Leverage: SBA 7(a) financing on early acquisitions, each loan capped at $5M, supplemented by seller financing as the platform scales
  • 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, funded by purchasing and central-fill savings before counting as net upside

Key Risks

  • PBM & DIR pressure → diversify into cash-pay clinical services
  • Generic deflation → purchasing scale plus a richer service mix
  • Integration & pharmacist retention → earnouts, retention packages, phased onboarding

Illustrative case study, not a live deal. Market context from NCPA/CMS public data; screening outputs are from my own proprietary, un-audited model.

Experience

A track record across the deal lifecycle.

Jan 2026 – PresentDeerfield, IL

Analyst → Associate

Walgreens · Finance & AI
  • Surfaced $14.5M in supply chain cost savings, plus a further $7M in additional savings, through data-driven analysis.
  • Built Ledger, a Python and Streamlit dashboard on Databricks that consolidates finance reporting, and wrote automated SQL queries to flag fraud.
  • Built the initial architecture for an automated continuous control monitoring pipeline, and shipped a Benford's Law anomaly-detection demo on Databricks' Genie AI/BI tool.
  • Led commodity-risk analysis on fleet diesel spend and automated forecasting with Excel VBA.
Aug 2025 – Dec 2025Miami, FL

Analyst

Transal Corp · Multi-Billion-Dollar Family Office
  • One of two investment professionals supporting a multi-billion-dollar family office portfolio alongside the CIO, screening opportunities and monitoring existing positions across private equity, venture capital, and private credit.
  • Screened and evaluated 130+ sponsor-led PE and co-investment opportunities across buyout, growth equity, and private credit, building return, leverage, and downside-case analysis to support the investment team.
  • Built IRR, MOIC, DPI, and TVPI attribution models benchmarking 40+ general partner track records against Cambridge Associates quartiles to support quarterly re-up and pacing analysis.
  • Developed a five-year capital-call and distribution-pacing model across the portfolio using Addepar for exposure monitoring, surfacing a funding gap that informed the 2026 commitment schedule.
Jun 2025 – Aug 2025Chicago, IL

Specialty Pharmacy Finance Intern

Walgreens
  • Engineered a SQL and 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, pharmacy account managers, and field leadership.
May 2024 – Oct 2024Miami, FL

Equity Analyst Intern

Maredin Wealth Advisors
  • Authored a long investment thesis on Snowflake Inc. (NYSE: SNOW) for an independent RIA, building a three-statement model, DCF, and bull/base/bear sensitivity analysis on platform economics and consumption-based pricing.
  • Defended the recommendation directly to the RIA's founder.
Jul 2023 – May 2024Chicago, IL

Algorithmic Trader

Core Value Capital LLC
  • Designed and backtested a systematic, mean-reversion FX trading strategy across 13 currency pairs using walk-forward cross-validation in Python.
  • Applied calculus-based optimization to refine algorithm parameters and improve strategy effectiveness across the portfolio.
  • Improved risk-adjusted returns by pruning 15% of currency pairs flagged for excess leverage and tail risk, tightening capital allocation across the remaining portfolio.
Contact

Let's connect.

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

Emailjosephd.mcdevitt@gmail.com
LinkedInjoseph-d-mcdevitt
GitHubjosephdmcdevitt-svg
RésuméDownload PDF