Develop and maintain forecasting models to predict customer support case volume, informing staffing, budgeting, and planning decisions at Google
Requirements
- Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience
- 4 years of experience in data science with a focus on time series analysis and forecasting
- Experience with Python or R programming with relevant forecasting libraries
- Experience in causal inference, A/B testing, statistical modeling, or machine learning
- Experience with forecasting methods, from classical statistical models to machine learning approaches
- PhD degree in a relevant quantitative field
- Experience with recent advancements in forecasting, such as foundation models (TimesFM) or deep learning approaches
Responsibilities
- 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 continuously, tracking key accuracy metrics, identifying model drift, and ensuring forecast reliability, and researching and implementing forecast 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 decision-making
Other
- Master's degree in a quantitative discipline
- 4 years of experience in data science
- Ability to apply judgmental forecasting and incorporate qualitative business adjustments into model outputs, especially for new or unprecedented events
- Disability or special need that requires accommodation
- Equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status
