About

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 specific 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 a range of 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 (preferably 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.

ABOUT THE JOB

• 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.

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 state-of-the-art 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 decision-making.