Staff Machine Learning Scientist - Personalization

Role Description

Penguin Random House is the largest trade publishing company in the world. The Data Science team is seeking a Staff Machine Learning Scientist to lead and advance the development of personalization products, including recommender systems for our websites, email programs, and online marketing. Personalization is a core growth lever for book discovery and customer engagement, directly improving how readers find the right books across every digital touchpoint.

Improving recommendation quality and relevance has a direct downstream impact on customer experience and business outcomes. This role will own personalization and recommender system work end-to-end, from model development to deployment to output monitoring, in close partnership with business stakeholders, platform engineers, and the rest of the personalization group.

Specific responsibilities include:

• Define and drive the technical roadmap for personalization and recommender systems, prioritizing roadmap items to meet business goals and defining short-term vision for the team.
• Propose and deliver R&D that directly shapes roadmaps, multiple projects, and long-term deliverables.
• Design and lead the development of software used by multiple teams, ensuring long-term maintainability, scalability, and adaptability.
• Ensure complex, multi-service personalization products meet SLAs and provide correct results over time.
• Establish and enforce experimentation best practices, including A/B testing frameworks, offline evaluation methodology, and metrics design across personalization surfaces.
• Lead team meetings, ensure the team's progress on the roadmap, and make technical decisions that unblock projects.
• Manage stakeholders' expectations with data-driven narratives and communicate effectively with senior leadership to align on strategy and track progress.
• Drive organizational efficiency and business impact by implementing new technologies and processes.
• Mentor senior and mid-level scientists, setting high code quality standards and best practices for the team.
• Stay current with advances in recommender systems, LLMs for personalization, and representation learning.

Qualifications

• PhD in Computer Science, Machine Learning, Engineering, Operations Research, Statistics, or a related quantitative field, OR Master's with 8+ years of applied ML experience.
• Deep expertise in recommender systems, personalization, ranking/retrieval, or computational advertising.
• Expert-level Python and deep proficiency with modern ML frameworks (PyTorch or TensorFlow) and recommendation-specific tooling (e.g., NVTabular, Merlin, Triton).
• Strong experience with cloud-based ML infrastructure (AWS, Kubernetes, Databricks), containerization (Docker), and model serving at low latency.
• Advanced SQL skills and experience architecting large-scale data pipelines and feature stores.
• Demonstrated ability to define technical roadmaps, influence direction across teams, and make architectural decisions that hold up over time.
• Excellent communication skills with the ability to present complex technical work to executive and non-technical audiences.
• Be cutting edge. Use the latest AI tools to develop well-designed and robust software.

Requirements

• Experience building and scaling real-time recommendation services handling millions of requests.
• Expertise in A/B testing methodology, causal inference, or experimentation platforms.
• Familiarity with LLM-based approaches to recommendation and content understanding.
• Experience with MLOps practices: model monitoring, feature stores, CI/CD for ML, and automated retraining pipelines.
• Prior experience technically leading a team of ML practitioners and setting standards adopted by others.

Benefits

• Medical/Prescription drug insurance
• Dental
• Vision
• Health Care/Dependent Care Flexible Spending Account
• Health Savings Account
• Pre-Tax and Roth 401(k)
• Short and Long-Term Disability Insurance
• Life/AD&D Insurance
• Commuter Benefits
• Student Loan Repayment Program
• Educational Assistance
• Generous paid time off