Asks for

The ad says“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.”
The ad says“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.”
The ad says“Strong command of the mathematical and statistical foundations behind machine learning, statistical inference, and experimental design.”
The ad says“Fluency in Python and its data and ML ecosystem (e.g., pandas/Polars, scikit-learn, PyTorch/JAX, XGBoost/LightGBM).”
The ad says“Fluency in Python and its data and ML ecosystem (e.g., pandas/Polars, scikit-learn, PyTorch/JAX, XGBoost/LightGBM).”
The ad says“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.”
The ad says“Strong command of the mathematical and statistical foundations behind machine learning, statistical inference, and experimental design.”
The ad says“Fluency in Python and its data and ML ecosystem (e.g., pandas/Polars, scikit-learn, PyTorch/JAX, XGBoost/LightGBM).”
The ad says“Fluency in Python and its data and ML ecosystem (e.g., pandas/Polars, scikit-learn, PyTorch/JAX, XGBoost/LightGBM).”
The ad says“Fluency in Python and its data and ML ecosystem (e.g., pandas/Polars, scikit-learn, PyTorch/JAX, XGBoost/LightGBM).”
The ad says“Fluency in Python and its data and ML ecosystem (e.g., pandas/Polars, scikit-learn, PyTorch/JAX, XGBoost/LightGBM).”
The ad says“Fluency in Python and its data and ML ecosystem (e.g., pandas/Polars, scikit-learn, PyTorch/JAX, XGBoost/LightGBM).”
The ad says“Strong command of the mathematical and statistical foundations behind machine learning, statistical inference, and experimental design.”
The ad says“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.”
The ad says“Fluency in Python and its data and ML ecosystem (e.g., pandas/Polars, scikit-learn, PyTorch/JAX, XGBoost/LightGBM).”

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. Our applied 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.

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.

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.

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

What we offer:

You’ll join an intellectually curious, interdisciplinary team that thrives on solving difficult problems and developing novel solutions that work in practice. We care deeply about our customers and build thoughtful products that deliver meaningful impact. You'll work alongside colleagues who take one another’s ideas seriously, challenge each other thoughtfully, and are eager to learn across disciplines. We offer a generous compensation package that includes a base salary, bonus, and equity, along with additional location-based benefits.

About QuantCo

QuantCo is an applied AI company. We build production systems that enable organizations to make and execute better decisions. Combining modern AI with causal inference, economic modeling, and optimization, we improve outcomes in high-stakes areas like pricing, underwriting, claims, and forecasting. Our customers include some of the largest insurers, financial institutions, retailers, and healthcare organizations in the US and Europe.

We bring together 250+ AI researchers, economists, data scientists, and software engineers to close the gap between what AI can predict and what organizations should do. Founded by four Harvard and Stanford PhDs and headquartered in Boston, we have offices in Berlin, Cologne, Karlsruhe, London, Munich, San Francisco, and Zurich.

QuantCo does not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.

Asks for

Values

You may be a strong fit if you have:

Nice to have: