Staff Machine Learning Scientist – Personalization (Open to Remote)

Penguin Random House US•New York, NY

22h•Remote

About The Position

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. We are investing in expanding our portfolio of business-critical personalization products and further improving our existing models. 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. We have a mature machine learning practice and strong infrastructure, supported by strong data warehouse and DevOps partners. We are transitioning to AI-accelerated development and use modern agentic coding tools like Claude Code to speed up how we build and maintain personalization systems, with rigorous quality gates including tests, reproducible workflows, and measurable improvements in model performance and reliability. Experience with Claude Code or agentic workflows is a plus, but we prioritize strong fundamentals and the ability and willingness to learn new workflows effectively.

Requirements

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, with a track record of shipping systems that operate at scale.
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.

Nice To Haves

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.
Experience with Claude Code or agentic workflows

Responsibilities

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. Models are used over the long term by multiple products and teams.
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.
Adapt systems to changing business needs and resolve multi-product, multi-team service incidents.
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.
Foster a collaborative and high-performance team culture.
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, bringing relevant advances into production when they deliver measurable improvement.