Seeking data analyst roles · June 2027 grad

Max Nudelman

I study Business Information and Analytics at the University of Denver and I'm looking for data analyst work after I graduate in 2027. Last summer I worked the risk desk at Fanatics, pricing live markets and deciding how much exposure to take on individual customers. Most of what I've built since is some version of checking whether a number means what it looks like it means.

Portrait of Max Nudelman

Selected work

What I've built

Seven projects across sports, health and the environment. Each one links to a full writeup with the method, the numbers, and the parts that didn't go to plan.

The Price That Isn't One

Complete

US hospital charges · 2,906 hospitals, 540 procedures

Every American hospital publishes what it charges, and that number decides what an uninsured patient owes. It explains about 6% of what is actually paid, and 85% of its variation is explained by which hospital you walked into rather than what was wrong with you. The median hospital bills 4.4 times what it collects.

R / tidyverseVariance decompositionCMS Medicare dataMeasurement validity
84.9%
of markup is the hospital
3.9%
is the procedure
4.4x
median markup
145,879
rows analysed
Read the write-up →

Who Actually Cut Their Emissions?

Complete

Two ways of counting the same thing · 118 countries, 1990 to 2023

Countries report the emissions released inside their borders, which is not the same as the emissions their consumption causes. Of the 40 countries that reported a cut, 34 saw the emissions driven by what they consume fall by less, and for 11 of them it rose. Globally the two measures are the same number, so the accounting does not hide emissions, it just decides who holds them.

R / tidyverseSensitivity analysisOur World in DataMeasurement validity
34 of 40
cuts overstated
39%
median overstatement
0.14%
change to the world total
Read the write-up →

What Empty Stadiums Revealed

Complete

Home advantage, decomposed · a natural experiment across 11 divisions

Home teams win more often, but the usual explanation is a list rather than an answer. COVID emptied every stadium in Europe at once, which let me test those explanations against each other. The home team's edge in shots halved. Its edge in fouls and cards vanished completely, and the same referees reversed their own bias.

R / tidyverseNatural experimentBootstrapHypothesis testing
31,356
matches
5.6pp
fall in home win rate
91%
of the card edge gone
11
divisions agreeing
Read the write-up →

Auditing My Own Analysis

Complete

Volleyball Nations League 2025 · finding the errors in my own work

I wrote a volleyball statistics analysis for a class, published it, and later found that one of its central metrics was mathematically impossible and one of its main conclusions was circular. I traced the first to a mislabelled column in the source data and the second to scoring each stat against a total that already contained it. This is the rebuild, and what the data actually says once both are fixed.

R / tidyverseggplot2Data validationCorrelation analysis
36
impossible values found
0.73 → -0.07
correlation once decircularised
305
players
Read the write-up →

World Cup 2026 Model

Live demo

Elo and Poisson forecasting · interactive dashboard

I built a forecasting model for the 48 team 2026 World Cup from scratch, starting with importance weighted Elo ratings fit over the full history of international results and feeding those into a ridge regularised Poisson attack and defence model that simulates every group and produces advancement odds. It runs as a self contained interactive dashboard.

PythonEloPoisson MLEMonte Carlo
49,417
matches fit
48
teams modeled
0
external libraries
Read the write-up →

Does the Market Know Something?

Complete

Odds movement and bookmaker margin · Premier League, 7 seasons

I took seven seasons of English Premier League prices from 16 bookmakers, stripped out the margin that every price has built into it, and tracked each one from the opening line to the close. Selections whose price shortened before kickoff beat their opening implied probability by 3.4 points, while the ones that drifted missed theirs by 5.0. The same result shows up independently at Bet365, Pinnacle and the best available price, which is the reason I think it's real.

R / tidyverseTableauDeviggingfootball-data.co.uk
2,660
matches
131,793
odds observations
16
bookmakers
+3.4pp
steamed-price edge
Read the write-up →

Stake Factor

Complete

Bettor segmentation and account risk policy · 7 European leagues

This is the call I was making by hand at Fanatics, done properly with data. Closing line value identifies an adverse account in five settled bets, while realized profit never does. The more useful finding is the second one: restricting too many people costs the book four times more than the sharps ever take.

DuckDB / SQLPythonClusteringSimulation
5 bets
to identify a sharp
never
using realized profit
−£202k
cost of over-restricting
Read the write-up →

Experience and education

Where I've worked and studied

June to August 2026

Associate Risk Trader

Fanatics Betting and Gaming
  • Traded 100+ of the 104 matches in the 2026 World Cup, correcting off lines and monitoring live pricing, pre match and in play, while managing customer exposure via stake factors.
  • Monitored live wagers to flag sharp betting patterns and promo abuse, escalating hundreds of high risk accounts and limiting repeat exposure.
  • Segmented bettors by stake profile and market preference to guide limit setting and pricing, improving hold on low and high confidence markets without losing recreational volume.
  • Used bettor psychology, including recency bias and favorite longshot tendencies as well as casual bettor tendencies, to adjust lines ahead of public money, reducing exposure and flagging customers whose behavior signaled risk.
  • The habit behind everything on this site started here. A restricted account was treated as a signal rather than just a closed door, and I wanted to see what that idea looked like done properly with data instead of by feel.
2023 to June 2027

B.S. Business Administration, Business Information and Analytics

University of Denver, Daniels College of Business
  • Minor in International Relations. GPA 3.81.
  • Coursework in database management, where I designed and built a normalized relational database from a set of requirements, along with statistical modeling, data visualization, and reproducible reporting in R and Quarto.

Toolkit

Tools I use

Data and query

  • SQL, including joins and window functions
  • DuckDB
  • Relational modeling and normalization
  • Excel (Advanced Certified)

Analysis

  • Python (pandas, scikit-learn)
  • R (tidyverse, ggplot2)
  • Quarto
  • Clustering and simulation

Visualization

  • Tableau & Tableau Public
  • ggplot2
  • Adobe Creative Suite

Methods

  • Natural experiments
  • Hypothesis testing and bootstrap
  • Probability calibration
  • Data validation and auditing

Contact

Let's talk

I'm looking for data analyst roles starting after I graduate in June 2027. I haven't settled on an industry yet, which is part of why these projects cover such different subjects. I'm based in Denver and happy to relocate, and email is the quickest way to reach me.