Note: The job is a remote job and is open to candidates in USA. Penguin Random House is a leading publishing company with a mature machine learning practice and strong data and DevOps infrastructure. The Machine Learning Scientist will own end-to-end forecasting systems, including feature engineering, model development, validation, deployment, monitoring, retraining, and reliability improvements. The role will also build evaluation and AI-assisted development workflows, translate business needs into technical roadmaps, and partner with operations, supply chain, inventory, finance, and marketing leaders.
Responsibilities
- 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
Skills
- • 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 PhD with 3+ years of applied experience
- • Experience building ML systems end-to-end (not just models): backtesting frameworks, scheduled retraining, monitoring/alerting, and automated reporting into planning or decision workflows
- • Demonstrated ability to inherit complex systems built by others and make sound architectural decisions with high autonomy
- • Technical leadership: raises the bar on evaluation, reproducibility, and production practices; mentors less-senior team members
- Location: Remote eligible (U.S.), but NYC area preferred
Benefits
- Remote eligible (U.S.), but NYC area preferred.
- All positions are currently eligible for an annual profit award or bonus, subject to company results.
Company Overview
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