Business Data Scientist, Forecasting, Google Cloud
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Minimum qualifications:
- 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).
About the job
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.
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, 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.
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