Quantitative Researcher

As a Quantitative Researcher at QuantCo, you’ll combine mathematical and statistical rigor with economic reasoning and modern machine learning to build models and systems that power high-stakes decisions across industries. Those systems tackle some of the hardest, most consequential problems our customers face, and you’ll carry the work from initial framing and research through experimentation, model development, and continued iteration in production. We work in areas such as algorithmic pricing, claims management, underwriting, and predictive health. You’ll build on a shared technology base that improves with each new application, while choosing—and when needed, developing—methods to fit the problem. That might mean designing and analyzing randomized experiments; estimating causal effects from messy observational data; building machine learning models that produce probabilistic forecasts; or formulating and solving optimization problems that balance competing economic objectives under real-world constraints. The resulting systems run in production at organizations serving millions of people, informing and automating critical business decisions that affect billions of dollars. You’ll have unusual autonomy over how they’re designed and built. There is no single path into this role. Our quantitative researchers come from economics, statistics, mathematics, physics, computer science, and other quantitative fields. The team brings together researchers with PhDs as well as those with master’s and bachelor’s degrees. What they share is exceptional quantitative judgment, the ability to learn quickly, and the drive to turn ideas into systems that deliver measurable real-world impact. You may be a strong fit if you have: Deep expertise in at least one relevant area—such as machine learning, statistics, econometrics, or causal inference—and an interest in learning and working beyond it. Strong command of the mathematical and statistical foundations behind machine learning, statistical inference, and experimental design. The ability to formulate testable hypotheses and draw reliable conclusions from complex, imperfect data. Fluency in Python and its data and ML ecosystem (e.g., pandas/Polars, scikit-learn, PyTorch/JAX, XGBoost/LightGBM). A degree in computer science, economics, statistics, mathematics, physics, engineering, or a related quantitative field. Nice to have: An advanced degree (MS/PhD) in one of the fields above. Experience evaluating models under uncertainty, distribution shift, or non-stationarity. Hands-on experience training, adapting, or evaluating deep learning and foundation models, or the curiosity and technical foundations to get there quickly. #J-18808-Ljbffr National Association for Business Economics