Staff/Senior Machine Learning Scientist - Pricing/Forecasting
New
U.S.-basedFull-TimeSenior
Salary180,000 - 250,000 USD per year
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLMachine LearningData scienceR
Requirements
- 5+ years of applied machine learning or data science experience with production model ownership.
- Strong experience in forecasting OR Bayesian/probabilistic modeling and statistical inference.
- Proven track record building and maintaining end-to-end ML systems in production environments.
- Deep understanding of time-series modeling, backtesting, and model evaluation techniques.
- Strong programming skills in Python (or R) and SQL, with production-quality coding practices.
- Solid foundation in statistics, including probabilistic reasoning and uncertainty quantification.
- Experience designing and deploying monitoring, retraining, and validation pipelines.
- Ability to communicate complex analytical concepts clearly to non-technical stakeholders.
- Experience collaborating across business, engineering, and product teams in cross-functional environments.
- Familiarity with AI-assisted development workflows and emphasis on reproducibility and quality control.
Responsibilities
- Own and operate end-to-end machine learning systems, including scoping, feature engineering, model development, deployment, monitoring, retraining, and ongoing performance improvements.
- Build and maintain production-grade evaluation frameworks, including backtesting systems, validation pipelines, error analysis, and uncertainty quantification.
- Develop scalable forecasting or pricing models depending on assignment, ensuring alignment with business objectives and operational constraints.
- Define success metrics tied to business outcomes and ensure model outputs are interpretable, reliable, and actionable for stakeholders.
- Design and implement AI-assisted development workflows to automate repetitive tasks while maintaining strict quality and validation standards.
- Write production-quality, testable, and reproducible code supporting robust ML pipelines.
- Partner with cross-functional teams to translate business requirements into prioritized ML roadmaps and measurable deliverables.
- Communicate model assumptions, limitations, and risks clearly to technical and non-technical stakeholders.
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