Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
3 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a relevant PhD degree.
3 years of experience in data science, with a focus on time series analysis and forecasting.
Experience in causal inference, A/B testing, statistical modeling, or machine learning.
Experience with a range of forecasting methods, from classical statistical models to machine learning approaches.
Preferred qualifications:
4 years of experience deploying and maintaining forecasting models in a live production environment.
Experience with recent advancements in forecasting, such as foundation models (TimesFM) or deep learning approaches.
Experience in a demand planning, contact center, or operational workforce management role.
Ability to apply judgmental forecasting and incorporate qualitative business adjustments into model outputs, especially for new or unprecedented events.
Familiarity with cloud platforms (e.g., Google Cloud Platform) and their AI/ML services (e.g., BigQuery, Vertex AI).