Sr Machine Learning Scientist - (Open to remote)

The Data Science team at Penguin Random House is seeking an experienced Senior Machine Learning Scientist to drive the development of personalization products, which includes recommender systems for our websites, email programs, and online marketing. 

 
As the world’s leading publishing house, Penguin Random House remains at the forefront of digital transformation, using cutting-edge AI and machine learning techniques to shape the future of book discovery, sales, and customer engagement. With a commitment to quality and innovation, we leverage data science to enhance our capabilities across pricing, forecasting, personalization, and more. 

 
Key Responsibilities: 

• Lead and own the design, development, and deployment of end-to-end machine learning projects for large-scale recommender systems and personalization products. 

• Develop models that power real-time online marketing tools, including customer segmentation, ad targeting, and user engagement prediction. 

• Design and run A/B tests to validate model performance, iterating based on experiment results and user feedback. 

• Collaborate with cross-functional teams including engineering, marketing, and product to integrate ML solutions into business products. 

• Stay up to date with industry trends and advancements in recommender systems, personalization, and AI-driven marketing technologies. 

Qualifications: 

• 5+ years of professional experience in machine learning, with a strong focus on recommender systems, personalization, and online marketing audience targeting models. 

• Expertise in Python and key ML libraries (e.g., TensorFlow/PyTorch, NVTabular, Triton). 

• Experience with cloud-based services (e.g., AWS, Kubernetes, Databricks), containerization (Docker), and deploying ML models at scale. 

• Strong knowledge of SQL for querying and managing large datasets. 

• Ability to communicate technical concepts and results effectively to non-technical stakeholders. 

Preferred Qualifications: 

• Master’s or PhD in a quantitative discipline like Statistics, Mathematics, Computer Science, Operations Research, or relevant work experience. 

• Proven track record of building, deploying, and optimizing large-scale machine learning models in production environments. 

• Experience in A/B testing, experimentation platforms, and online learning. 

• Familiarity with MLOps tools and practices for managing the lifecycle of machine learning models in production.

…