Applied Scientist, AI/ML

As an Applied Scientist, AI/ML at QuantCo, you'll bring together advances in AI with rigorous economic and quantitative thinking 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 experiments; combining predictive modeling with causal inference; developing multimodal models that combine images, text, and structured data; training foundation models on sequences of medical events; or building agents that work with complex data and workflows. 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. Ourapplied scientists come from AI research, economics, statistics, computer science, and other quantitative fields. The team brings together colleagues 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.

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.

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.

An advanced degree (MS/PhD) in one of the fields above.

Hands-on experience training, adapting, or evaluating deep learning and foundation models, or the curiosity and technical foundations to get there quickly.

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