Minimum qualifications:
- 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.
Preferred qualifications:
- PhD degree in a relevant quantitative field.
- 6 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.
- Familiarity with cloud platforms (e.g., Google Cloud Platform) and their AI/ML services (e.g., BigQuery, Vertex AI).
- Ability to apply judgmental forecasting and incorporate qualitative business adjustments into model outputs, especially for new or unprecedented events.
In this role, you will develop and maintain forecasting models that predict customer support case volume. Your work will directly inform staffing, budgeting, and planning decisions, enabling the delivery of exceptional customer support at scale.
The US base salary range for this full-time position is $166,000-$244,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about
benefits at Google.
- 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.
