Hi! My name is Zhiyu Guo, though I also go by Jessica. I’m a data scientist and statistician specializing in causal inference, fairness analysis, and text analysis.

Background

I completed doctoral coursework in Statistics & Data Science and Public Policy at Carnegie Mellon University (2021-2025), earning an M.Phil. in Public Policy and Management and M.S. in Statistics. I also hold a B.A. in Mathematics (cum laude) from New York University (2021).

Research & Professional Experience

My work focuses on applying rigorous statistical methods to address fairness and disparities:

  • Criminal Justice: Analyzed racial disparities in jury selection using doubly robust causal inference (JSM 2025)
  • Algorithmic Fairness: Evaluated housing assessment algorithms for effectiveness and equity
  • Text Analysis: Assessed gender disparities in social media using advanced matching methods on 97K posts (ACIC 2023)
  • Pharmaceutical Industry: Built Bayesian predictive models for drug pipeline optimization at Regeneron Pharmaceuticals

I’ve published in peer-reviewed journals, presented at major statistical conferences, and taught 200+ graduate students as a TA for 8+ courses at CMU.

What I’m Looking For

I’m seeking data science, data analytics, or research positions where I can apply statistical rigor to solve meaningful problems. I’m particularly interested in:

  • Organizations working on social impact, policy, or fairness issues
  • Teams developing ethical AI/ML systems
  • Research-oriented roles in tech, consulting, or policy
  • Positions valuing statistical thinking and clear communication

Skills & Expertise

Methods: Causal inference, machine learning, survival analysis, text/NLP, Bayesian methods, sensitivity analysis, program evaluation

Programming: R, Python (Pandas, NumPy, scikit-learn, PyTorch), SQL, MATLAB, Git, Tableau

Domain: Algorithmic fairness, criminal justice statistics, pharmaceutical analytics, social media analysis, large-scale data processing

Communication: Technical writing, executive presentations, teaching, translating complex findings for diverse audiences

Feel free to get in touch to discuss opportunities or collaborations!