Senior Applied Scientist

You bring 5+ years of experience in applied machine learning, recommendation systems, search/ranking, experimentation, or a closely related area, with a track record of driving measurable product impact.

You have strong foundations in machine learning, statistics, experimental design, and causal thinking, and you know how to choose the right level of modeling complexity for the problem at hand.

You have hands-on experience with at least some of the following: collaborative filtering, retrieval and ranking systems, representation learning, sequence / generative models, bandits, graph methods, or personalization for consumer products.

You are highly proficient in Python and comfortable working with common ML libraries such as PyTorch, TensorFlow, Scikit-learn, XGBoost, or similar tooling.

Experience working with SQL, distributed data processing, and cloud-based ML workflows is strongly preferred.

Experience personalizing content, commerce, media, entertainment, gaming, or subscription products at scale.

In the role of Senior Applied Scientist for Recommendation and Personalization, you will report to the Director of Data Science and Machine Learning in our Center for Data and Insights.

This role is ideal for someone who enjoys combining strong scientific rigor with product thinking to improve user discovery, engagement, retention, and long-term fan value.

You will work across multiple user touchpoints, including app and web interfaces, lifecycle and promotional email campaigns, and flywheels that connect video, ecommerce, manga, and adjacent experiences.

You will help define what great personalization looks like at Crunchyroll, build the evidence to prove impact, and collaborate with engineering partners to ensure the resulting solutions can be productionized effectively.

Our centralized DS/ML team serves stakeholders across Finance, Product, Engineering, Marketing, Creatives, and Content Operations with data-driven and ML/AI-powered solutions. Within that broader organization, the Personalization and Recommendation group is building the next generation capabilities to power tailored fan experiences across every major user interface and lifecycle touchpoint. Today, the team includes engineers focused on operationalizing our recommendation platform with strong engineering excellence. This Applied Scientist role complements that foundation by bringing deeper scientific ownership to modeling strategy, evaluation, and experimentation, while partnering closely with an additional MLE hire to accelerate production impact.