Business Data Scientist, Forecasting, Cloud

• 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.

• 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).

In this role, you will be responsible for developing and maintaining the models that predict our customer support case volume. Your work will be a critical input for the organization's staffing, budgeting, and strategic planning, directly impacting our ability to deliver exceptional customer support at scale.

• Develop, deploy, and maintain time series forecasting models to predict customer support case volumes across various products, regions, and channels.
• Build and automate scalable data pipelines to ensure timely and reliable data for model training and inference.
• Monitor and evaluate model performance, dealing with key accuracy metrics, identifying model drift, and ensuring forecast reliability. Research and implement forecasting techniques to continuously improve model accuracy and capabilities.
• Partner with Operations, Finance, and leadership stakeholders to understand their planning needs, deliver forecasts, and explain variance drivers.
• Communicate forecast results and uncertainty to both technical and non-technical audiences to guide strategic decision-making.