Lead Data Scientist (Forecasting)
Skills
- Deep expertise in time-series forecasting (classical and deep-learning approaches).
- Practical experience with Bayesian modeling and probabilistic programming (e.g., PyMC3, Stan).
- Proficiency in Python, R, SQL and ML/DL frameworks (PyTorch, TensorFlow, scikit-learn, DARTS).
- Familiarity with cloud and MLOps tooling (AWS SageMaker, MLflow) for scalable deployment.
- Strong experimental design, causal inference, and model evaluation skills.
- Excellent communication and stakeholder-facing presentation skills.
Responsibilities
- Design, develop, and deploy forecasting and pricing models using classical time-series, deep learning, and Bayesian approaches.
- Build hierarchical and multi-level Bayesian frameworks that scale across thousands of individual hotel models.
- Implement uncertainty quantification and probabilistic forecasts to improve trust and operational robustness.
- Collaborate with engineering to productionize models (e.g., via AWS SageMaker) and establish monitoring and CI/CD for models.
- Translate model outputs into actionable insights with product and business stakeholders to inform pricing and inventory decisions.
- Define and run experiments, evaluation metrics, and causal/impact measurement strategies (including uplift analyses).
- Present modeling results, roadmap, and recommendations to senior leadership and cross-functional teams.
Education
- MS or PhD in Statistics, Econometrics, Computer Science, Operations Research, or related quantitative field.
Benefits
- Competitive salary and equity/package aligned with high-growth company.
- Flexible, collaborative, and casual work environment with hybrid/remote options.
- Health, dental, and vision benefits.
- Retirement plan (e.g., 401(k)) and paid time off.
- Professional development budget and conference support.
- Opportunity to influence product and business strategy at a growing SaaS company.
Average salary estimate
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