Machine Learning Scientist, Pricing/Personalization (Open to Remote)

The Data Science team at Penguin Random House is seeking two Machine Learning Scientists to build and improve machine learning products that power how we understand demand, set strategy, and connect readers with books.  

 

We’re hiring across both our recommendation and pricing teams. In this role, you’ll use rich datasets to build sophisticated models that either recommend books to readers or help price books for the market. Our recommendation systems drive discovery across our digital channels and marketing, while our pricing products model market response to optimize revenue and mitigate risks to sales and brands. 

 

As the world’s leading publishing house, Penguin Random House is AI-forward and continues to invest in modern machine learning to shape the future of book discovery, sales, and customer engagement. Our Data Science team applies ML across personalization, forecasting, pricing, and experimentation, and we actively explore emerging AI capabilities when they can meaningfully improve customer experiences. 

 

This role spans the ML lifecycle; from problem framing and prototyping to production and iteration, with a strong emphasis on writing robust, maintainable software and using modern AI tools thoughtfully to accelerate development. 

 

Specific responsibilities include:  

• Owns the ML lifecycle: Frames problems, prototypes solutions, validates performance, and iterates based on measurement and feedback. 

• Applies sound statistical and ML practices for feature development, training, evaluation, validation, and maintenance. 

• Defines success metrics and uses offline evaluation plus online experimentation (including A/B tests) to validate performance, monitor quality over time, and drive ongoing iteration. 

• Partners with engineering and platform counterparts to productionize models, including repeatable training, deployment workflows, monitoring, and refresh strategies. 

• Diagnoses model performance issues and data quality problems; communicates findings and recommended actions clearly to stakeholders. 

• Writes maintainable, production-minded code and contributes to team standards (code review, documentation, reproducibility, and testing where appropriate). 

• Stays current on applied ML and uses modern AI tools to accelerate exploration, questioning, and implementation without sacrificing quality. 

 

Please apply if you meet the following qualifications:  

• Master’s with 2+ years of applied work experience OR PhD in Computer Science, Machine Learning, Engineering, Operations Research, Statistics or a related quantitative field 

• Strong proficiency in Python and common ML frameworks and libraries (for example PyTorch or TensorFlow). 

• Strong SQL skills and experience working with large datasets for feature development, analysis, and validation. 

• Experience taking models from development to production (batch scoring, APIs, or downstream integration). 

• Solid understanding of experimentation and measurement. 

• Be cutting edge. Use the latest AI tools to develop well-designed and robust software. 

• Strong communication skills and ability to translate between business goals and technical solutions.  

• Familiarity with cloud and modern data/ML tooling (AWS, Databricks, Docker, Kubernetes, Spark, or similar). 

• Exposure to MLOps concepts and tooling (model registries, pipelines, monitoring, reproducibility). 

 

Preferred qualifications:  

We welcome candidates with depth in either (or both) of these applied ML areas: 

Forecasting 

• Experience with time series forecasting, causal/market-response modeling, optimization, or risk-aware modeling. 

• Familiarity with automated model retraining, monitoring, and long-running model maintenance. 

Personalization 

• Experience with recommender systems, ranking/retrieval, personalization, segmentation, prope