Staff/Senior Machine Learning Scientist - Forecasting
New
P
Penguin Random HousePublishing
Remote eligible (U.S.)Full-TimeSenior
SalarySenior level is $180,000–$220,000. The Staff level is $210,000–$250,000.
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Job Details
- Experience
- 5+ years for Senior; 8+ years (or PhD with 3+ years) for Staff
- Required Skills
- PythonSQLMachine LearningR
Requirements
- 5+ years (Senior) or 8+ years (Staff) in applied ML/data science.
- Experience owning models in production, including deployment, monitoring, incident response, and retraining.
- Strong forecasting expertise in time-series methods and feature engineering OR deep expertise in Bayesian statistical methods and probabilistic programming.
- Strong statistics fundamentals and comfort with probabilistic forecasting.
- Strong Python (or R) and SQL skills; ability to write production-quality, testable code.
- Experience using AI-assisted development workflows responsibly.
- Strong communication and cross-functional collaboration skills.
- Staff level: Experience building ML systems end-to-end and demonstrated ability to inherit complex systems with high autonomy.
- Staff level: Proven technical leadership in mentoring and improving production/evaluation practices.
Responsibilities
- Own end-to-end ML systems: scoping, feature engineering, model development, backtesting/validation, deployment, 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 to stakeholders.
- Write production-quality, testable code and support reproducible workflows.
- Build and improve forecasts across time horizons and business segments (demand, inventory, supply chain, resource allocation).
- Productize forecast outputs for stakeholders, including reporting that explains changes, model performance, and uncertainty.
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