I’m Joseph Linthicum, a junior at Virginia Tech’s Pamplin College of Business majoring in Management Consulting & Analytics. I’ve gained hands-on experience through internships and project work, developing skills in consumer insights, strategy, and analytics.
I was born in the United States but spent much of my upbringing in Germany, returning to the U.S. to pursue my education full-time. These international experiences shaped my adaptability and global outlook, giving me a deeper understanding of markets and consumer behavior. I now bring this perspective to my academic work and professional goals, and I’m eager to apply it to a career in consulting, finance, or analytics.
Here are some of my consulting projects and examples of the frameworks I use. These include case studies completed at Virginia Tech, Passion projects, and team-based consulting work, where I applied structured problem-solving approaches such as the Pyramid Principle, MECE, and SCQA.
Using a MECE, Pyramid-Principle workflow, I built a financial spine for ESPN’s DTC by synthesizing earnings disclosures, launch announcements, and viewing-share datasets, then decomposed value leakage across rights economics, distribution reach, and monetization (ARPU, ad yield, churn).
I quantified impacts via an EBIT bridge and Base/Up/Down scenarios, pressure-testing with sensitivity tornadoes on churn, rights CAGR, and ad CPM, and validating with cohort-style retention and LTV/CAC unit-economics.
Insights translated into a 24-month execution roadmap with owners, decision gates, KPI guardrails, and risk triggers, anchored on rights-sharing, bundled distribution expansion, and identity-driven ad-tech uplift.
Leveraging a MECE, Pyramid-Principle workflow, I built a data-driven demand model of UFC PPVs (2020–2025) using industry-reported buy estimates and engineered features for star power, title density, narrative heat, and undercard depth.
I ran log-linear OLS with robustness checks (outlier trims, alternative specs) to quantify uplift elasticities by driver, then pressure-tested insights via scenario analysis and a revenue bridge (Base/Up/Down) with sensitivity tornadoes.
Outputs convert analytics to action: a tiered-pricing & bundling playbook, calendar “anchor & fill” sequencing, and KPI guardrails (buys, ARPU, CAC/LTV), executive-ready for commercialization.
Here is a completed a range of data science projects focused on forecasting, customer analytics, financial modeling, and consulting case studies. From building predictive models that improve retail sales forecasting and portfolio risk management, to uncovering customer segments that drive targeted marketing, to solving operational challenges in case competitions, these projects highlight my ability to combine technical skills with business insights.