About

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.