Derek Grifka

Experience

Chicago Fire FC

March 2025 - Present

Data Scientist: Writing statistical models to evaluate global soccer talent.

Molson Coors Beverage Company

June 2021 - March 2025

Pricing Strategy and Analytics Manager: Leveraged advanced analytics, including Bayesian methods and machine learning to improve pricing strategies and developed visualization tools for sales teams.

Category Management Analyst: Developed a recommender system and customer-facing website using clustering algorithms to optimize product placement and sales across retail locations.

Aramark Corporation

December 2019 - May 2021

Led data integration and analytics initiatives using Python, PostgreSQL, and Power BI to improve institutional performance metrics across Chicago Public Schools.

Ziegler Capital Management

June 2018 - November 2019

Performed quantitative analysis of investment-grade corporate bonds using, while monitoring key credit metrics for the fixed income team.

Marquette University

Graduated 2018

Earned BS in Finance and International Business from Marquette University (2018), participating in the Applied Investment Management Program

Other Experience

Victory Analytics LLC — Founder, Data Analytics Consultant
September 2023 - Present Provide statistical analysis for a professional baseball consulting company using Pybaseball. Developed a GB Classifier model for simulating MLB game outcomes through batted ball resampling (https://x.com/mlb_simulator).

MIT Applied Data Science Program
September 2022 - December 2022 Completed comprehensive training in data science, machine learning, and deep learning, culminating in a capstone project developing an image detection model. Program covered Python fundamentals, statistical analysis, supervised learning, neural networks, and recommendation systems.

Research

Who Deserved to Win? Building an MLB Game Outcome Simulator

Published on Medium

A deep dive into creating a statistically-driven MLB game outcome simulator that determines which team truly deserved to win based on batted ball data and game situations.

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Overlap: How can pre-snap motion exploit it?

Published on Kaggle

An analysis of how pre-snap motion can affect NFL coverage types.

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Applying Bayesian Hierarchical Methods to MLB Season Win Probabilities with PyStan

Published on Medium

An exploration of using batted ball data with Bayesian hierarchical modeling to evaluate MLB team performance.

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MLB Game Simulator Bot

A Twitter bot that simulates MLB game outcomes using advanced statistical modeling and batted ball resampling techniques. Code can be found here (https://github.com/dgrifka/baseball_game_simulator).

View MLB Simulator Bot