About the job
We are looking for a Machine Learning Scientist to join our team and bring deep ML and causal inference rigor to some of the hardest quantitative problems in subscription pricing. You will collaborate with other researchers to advance our causal measurement capabilities, own complex ML initiatives end-to-end, and bring deep technical rigor to some of the hardest quantitative problems in subscription pricing.
Responsibilities
Design and implement quasi-experimental and causal inference approaches (difference-in-differences, synthetic control, instrumental variables, and related QED methods) to measure the true impact of pricing actions in observational, global datasets
Build and productionize measurement models and causal inference pipelines that estimate how pricing actions affect member behavior - from feature engineering through deployment, monitoring, and iteration
Conduct elasticity and willingness-to-pay research to deepen our understanding of member price sensitivity across global markets
Evolve our core measurement and analytics tools, integrating new science as the field advances
Partner with Finance & Strategy and Product leadership to translate statistical findings - including uncertainty - into business recommendations; push back constructively when business assumptions conflict with statistical evidence
Own your work all the way through: from ideation to production systems to learning from real-world outcomes
Qualifications
Minimum
You have deep expertise in causal inference and quasi-experimental design - you can distinguish true pricing impact from correlation in messy, global observational data, and you know when findings are conclusive versus when they are not
You have a proven track record of taking ML initiatives from 0 to 1, including building, deploying, and maintaining production models
You are proficient in Python, with experience in ML and statistical libraries (e.g., scikit-learn, PyTorch, TensorFlow, or JAX)
You have experience in B2C subscription businesses and an intuition for how pricing decisions play out at scale
You are a clear communicator - you can explain complex causal and ML methodology to non-technical audiences, present uncertainty alongside conclusions, and influence decisions with rigor rather than false confidence
You are a first-principles thinker: you identify the right question before choosing the method, and you are naturally skeptical of correlational claims in pricing data
Preferred
You have an advanced degree (MS or PhD) in statistics, economics, computer science, mathematics, or a related quantitative field, or equivalent applied research experience