Senior Machine Learning Scientist - Pricing​/Forecasting; Remote

Staff/Senior Machine Learning Scientist – Pricing/Forecasting (Open to Remote)

Penguin Random House is the largest trade publishing company in the world. The Data Science team is seeking two experienced Machine Learning Scientists to drive business‑critical forecasting and pricing products — one focused on demand forecasting and one focused on e‑book pricing optimization.

We have a mature machine learning practice with strong infrastructure, supported by data warehouse and Dev Ops 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 ML systems, with rigorous quality gates including tests, reproducible workflows, and measurable improvements in model performance and reliability.

Both roles may be filled at the Senior or Staff level depending on experience and interview performance.

Location:

Remote eligible (U.S.), but NYC area preferred

• Own end‑to‑end ML systems: scoping, feature engineering, model development, backtesting/validation, deployment (with platform partners), monitoring/alerting, retraining cadence, and ongoing reliability improvements.
• Create and maintain production‑safe evaluation infrastructure: automated backtests, error decomposition, uncertainty quantification, data validation, regression gates, and auditable model/version lineage.
• Build AI‑assisted/agentic development workflows (e.g., Claude Code) to automate repetitive tasks with human review and measurable quality gates.
• Define success metrics tied to business outcomes; communicate assumptions, limitations, and risk so model outputs are used correctly by stakeholders.
• Write production‑quality, testable code and support reproducible workflows.
• Partner across functions to translate business needs into a prioritized technical roadmap and measurable impact.

• Build and improve forecasts across time horizons and business segments (demand, inventory, supply chain, resource allocation), selecting approaches that balance accuracy, stability, interpretability, and operational cost.
• Productize forecast outputs for stakeholders: clear definitions and assumptions, versioned releases, and reporting that explains what changed, why it changed, and how uncertainty should shape decisions.
• Feature engineering, uncertainty quantification and calibration, hierarchical/segmented forecasting where appropriate.
• Partner with operations, supply chain, inventory, finance, and marketing leaders.

• Full technical ownership of our automated e‑book pricing system, including model architecture (Bayesian hierarchical models), inference pipelines, and decision logic.
• Design, run, and analyze backtests and live A/B tests to validate pricing strategies and measure real‑world revenue impact.
• Evaluate when to enhance the existing system versus migrate to new approaches; make and execute on architectural recommendations.
• Balance revenue optimization with brand considerations and sales stability.
• Collaborate with the print book pricing team, identifying transferable techniques and shared infrastructure.

• 5+ years in applied ML/data science, including owning models in production (deployment, monitoring, incident response, retraining).
• Strong forecasting expertise (time‑series methods, feature engineering, rigorous backtesting) OR deep expertise in Bayesian statistical methods and probabilistic programming.
• Strong statistics fundamentals; comfort with probabilistic forecasting and explaining uncertainty in practical terms.
• Strong Python (or R) and SQL; writes production‑quality, testable code.
• Strong communication and cross‑functional collaboration with non‑technical stakeholders.
• Experience using AI‑assisted development workflows responsibly (verification loops, reproducibility, automated checks).

• 8+ years in applied ML/data science, or Ph.D. with 3+ years of applied experience.
• Experience building ML systems end‑to‑end (not just models): backtesting frameworks, scheduled retraining, monitoring/alerting,…