Senior Applied Scientist

About Crunchyroll Founded by fans, Crunchyroll delivers the art and culture of anime to a passionate community. We serve over 100 million anime and manga fans across 200+ countries, helping them connect with the stories and characters they love through streaming, games, merchandise, events, and more. Join our team and help shape the future of anime! About the role We are hiring an Applied Scientist to advance personalization across the Crunchyroll ecosystem. You will lead scientific development of recommendation, ranking, and decision‑making solutions that improve how fans discover and engage with anime series, movies, manga, merchandise, games, and other fan experiences. You will partner closely with Machine Learning Engineers, Product, Engineering, Marketing, and Content stakeholders to make Crunchyroll the ultimate destination for anime. This role reports to the Director of Data Science and Machine Learning in our Center for Data and Insights. It is ideal for someone who enjoys combining scientific rigor with product thinking to improve user discovery, engagement, retention, and long‑term fan value. You will work across app, web, and lifecycle touchpoints—email campaigns, promotional offers, and joyful flywheels that connect video, e‑commerce, manga, and adjacent experiences. You will define what great personalization looks like, build evidence to prove impact, and collaborate with engineering to production‑ize solutions. Location: Los Angeles (primary) or San Francisco (secondary). Hybrid schedule: in‑office Tuesday‑Thursday, remote days as needed. Core areas of responsibility Lead research and development of recommendation, ranking, retrieval, and personalization methods tailored to streaming, manga, e‑commerce, and lifecycle marketing. Frame ambiguous business and product questions into clear scientific problems, hypotheses, success metrics, and experimentation plans. Design and run robust offline evaluation frameworks covering relevance, diversity, novelty, coverage, calibration, and long‑term value metrics. Partner with Product, Analytics, and Engineering to define online experiments, interpret results, and translate 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 collaborative filtering, content‑based methods, sequence modeling, deep learning, bandits, causal or uplift methods, and LLM‑enabled recommendation techniques. 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 trade‑offs, 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. About you We get excited about candidates because you have: Experience: 5+ years in applied machine learning, recommendation systems, search/ranking, or experimentation, with a track record of driving measurable product impact. Scientific depth: Strong foundations in machine learning, statistics, experimental design, and causal reasoning, and the ability to choose appropriate modeling complexity. Recommendation expertise: Hands‑on experience with collaborative filtering, retrieval, ranking systems, representation learning, sequence or generative models, bandits, graph methods, or personalization for consumer products. Technical skills: Proficient in Python and common ML libraries (PyTorch, TensorFlow, Scikit‑learn, XGBoost, etc.). Preferred skills include SQL, distributed data processing, and cloud‑based ML workflows. Experimentation mindset: Ability to design offline and online evaluations, reason about metrics, and connect experimental findings to user and business outcomes. Cross‑functional collaboration: Experience partnering with engineers, product managers, analysts, marketers, and business stakeholders to move concepts to execution. Communication skills: Ability to explain sophisticated modeling decisions and ambiguous findings in a clear, decision‑oriented way to diverse audiences. Educational background: MS or PhD in Computer Science, Machine Learning, Statistics, Operations Research, Economics, or a related quantitative discipline, or equivalent industry experience. Nice to have 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. About the team Our centralized DS/ML team serves stakeholders across Finance, Product, Engineering, Marketing, Creatives, and Content Operations. Within the broader organization, the Personalization and Recommendation group is building next‑generation capabilities to power tailored fan experiences across every major user interface and lifecycle touchpoint. Today, the team includes engineers focused on operationalizing a 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. Why this role is exciting Define personalization strategy across multiple surfaces instead of optimizing a single narrow funnel. Work on problems spanning content discovery, user engagement, retention, and cross‑domain ecosystem value. Influence what we build and how we measure success, with strong exposure to partners across the business. Join at a formative moment, shaping team standards, technical direction, and long‑term roadmap. Why you will love working at Crunchyroll In addition to working with fun, passionate, and inspired colleagues, you will enjoy the following benefits and perks: Competitive compensation package including salary and performance‑based bonus potential. Flexible time‑off policies. Generous medical, dental, vision, STD, LTD, and life insurance. Health Savings Account (HSA) and dependent‑care Flexible Spending Account (FSA). 401(k) plan with employer match. Support program for new parents. Pet insurance; pet‑friendly offices. Pay Range Annual salary range: $185,000 – $245,000 USD. Pay is based on location, experience, and performance, and is part of Crunchyroll’s total rewards offering. Our commitment to diversity and inclusion Our mission of helping people belong reflects our commitment to diversity and inclusion. We are an equal‑opportunity employer and value diversity at Crunchyroll. Pursuant to applicable law, we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. #J-18808-Ljbffr