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. 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. - 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. 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, 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. 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

• Develop and deploy Artificial Intelligence applications, including intelligent agents, ensuring code quality, scalability, and maintainability; • Build and expose inference endpoints for consumption by different applications and services; • Implement MLOps practices, including experiment tracking, model versioning, CI/CD and automated testing; • Develop and maintain data and training pipelines, including data validation, feature extraction and drift monitoring; • Integrate Machine Learning models with APIs, gateways, authentication mechanisms and observability tools; • Optimize performance and costs of training and inference processes through appropriate processing techniques and resource utilization; • Support and maintain AI solutions, assisting in incident resolution, root cause analysis and reliability improvements; • Produce technical documentation, diagrams and runbooks to support solution maintenance and evolution; • Participate in code reviews and knowledge-sharing initiatives across teams; • Collaborate with Product, Data, Platform and Engineering teams to define and implement solutions; • Ensure adoption of best practices for security, privacy, compliance and Responsible AI throughout the development lifecycle.

• Own the roadmap for Vegetation Intelligence, spanning from the ML models that produce our risk data to the products that turn it into customer action. • Push our detection models further, and help fold their outputs into Overstory’s broader outage and ignition risk models. • Contribute towards increasing the number of actionable insights: run user research, ground priorities in the operational decisions customers actually make, and make sure what we ship drives real action in the field. • Shift the roadmap from scaling- and technical-led to customer-driven, without losing the technical rigor that got us here. • Work closely with data scientists and ML engineers to turn model capability into shipped product value, and define what “good” looks like for a model in production. • Explore how feedback from customers and end users, like arborists, can improve model performance over time. • Communicate clearly and often with the broader product and engineering team, so trade-offs, model limits, and priorities stay visible. • Contribute to Overstory’s broader product strategy so Vegetation Insights supports where the rest of the product is headed

• Own the full ML lifecycle from data collection to ongoing quality assessment. • Train CV/ML models to detect deepfakes and uncover complex anomaly patterns. • Balance research and speed in model building and heuristic solutions. • Serve models efficiently with FastAPI and PyTorch, integrating with Java backend. • Build and monitor ML pipelines and dashboards with tools like Airflow and ClickHouse.