Senior Applied Scientist

We are hiring an Applied Scientist to help advance personalization across the Crunchyroll ecosystem. In this role you will lead the scientific development of recommendation ranking and decisioning solutions that improve how fans discover and engage with anime series/movies manga merchandise games and other areas in the anime fandom. You will partner closely with Machine Learning Engineers Product Engineering Marketing and Content stakeholders to make Crunchyroll the ultimate destination for anime experience.

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. You will own the research and applied science agenda for personalization from problem framing and data exploration through model development evaluation experimentation and iteration. 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.
This position is based in our Los Angeles office but we will consider our San Francisco office as a secondary location. We work a hybrid schedule in-office three days a week: Tuesday Wednesday and Thursday. 

• Lead the research and development of recommendation ranking retrieval and personalization methods tailored to Crunchyroll use cases across streaming manga ecommerce and lifecycle marketing surfaces.
• Frame ambiguous business and product questions into clear scientific problems hypotheses success metrics and experimentation plans.
• Design and run robust offline evaluation frameworks for recommender systems including relevance diversity novelty coverage calibration and long-term value metrics.
• Partner with Product Analytics and Engineering to define online experiments interpret results and turn learnings into roadmap decisions and model improvements.
• Develop user content and contextual understanding through feature design representation learning segmentation and behavioral analysis.
• Prototype and evaluate a range of approaches including collaborative filtering content-based methods sequence modeling deep learning bandits causal or uplift methods and LLM-enabled recommendation techniques where appropriate.
• Analyze user feedback loops and cross-domain interactions to improve discovery across video merchandise manga and other ecosystem experiences.
• Work closely with Machine Learning Engineers to translate promising research into production-ready solutions on our in-house recommendation platform.
• Communicate scientific findings model tradeoffs and business implications clearly to technical and non-technical stakeholders.
• Help establish best practices for experimentation reproducibility model governance and scientific documentation within the personalization and recommendations function.

We get excited about candidates like you because you have:

Experience: 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.

Scientific Depth: 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.

Recommendation Expertise: 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.

Technical Skills: 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.

Experimentation Mindset: You know how to design offline and online evaluations reason carefully about metrics and connect experimental findings to user and business outcomes.

Cross-Functional Collaboration: You have experience partnering effectively with engineers product managers analysts marketers and business stakeholders to move from idea to execution.

Communication Skills: You can explain sophisticated modeling decisions and ambiguous findings in a clear decision-oriented way to diverse audiences.

Educational Background: You hold an MS or PhD in Computer Science Machine Learning Statistics Operations Research Economics or a related quantitative discipline or you bring equivalent applied industry experience.

• Experience personalizing content commerce media entertainment gaming or subscription products at scale.
• Familiarity with recommender-system failure modes such as popularity bias cold start sparse feedback and feedback loop effects.
• Experience with multi-objective optimization constrained ranking or balancing short-term engagement with long-term user value.
• Exposure to generative AI representation learning or LLM applications that support recommendation and personalization workflows.
• Published research patents or open-source contributions in recommendation systems personalization applied machine learning or experimentation.

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.

• You will help define personalization strategy across multiple surfaces instead of optimizing a single narrow funnel.
• You will work on problems that span content discovery user engagement retention and cross-domain ecosystem value.
• You will influence both what we build and how we measure success with strong exposure to partners across the business.
• You will join at a formative moment with the opportunity to shape team standards technical direction and long-term roadmap.

In addition to getting to work with fun passionate and inspired colleagues you will also enjoy the following benefits and perks:

#LifeAtCrunchyroll #LI-Hybrid

What We Do

At Crunchyroll we deliver what anime fans love—anytime anywhere. With the world’s largest anime streaming library we connect fans to the stories characters and creators they love. But Crunchyroll is more than just a destination to watch anime—it's a global ecosystem where anime lives and breathes beyond the screen. From streaming and theatrical releases to merch games news events and music we offer fans immersive experiences that celebrate anime culture in all its forms. Headquartered in the U.S. with teams and reach across the globe Crunchyroll is an independently operated joint venture between Sony Pictures Entertainment and Aniplex of Japan. This unique partnership gives us the power to scale globally while staying rooted in anime’s cultural origins ensuring authenticity and access for fans everywhere. We believe anime is more than entertainment—it’s a way of life. And we’re here to champion that passion every day.

Why Work With Us

We take the People Experience seriously here at Crunchyroll. Join a diverse team of talented ambitious people who treat each other well and believe that how we win is just as important as winning.

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