MINIMUM QUALIFICATIONS:
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Master's degree in a quantitative discipline such as Statistics, Engineering,
Sciences, or equivalent practical experience.
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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.
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3 years of experience in data science, with a focus on time series analysis
and forecasting.
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Experience in causal inference, A/B testing, statistical modeling, or machine
learning.
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Experience with a range of forecasting methods, from classical statistical
models to machine learning approaches.
PREFERRED QUALIFICATIONS:
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4 years of experience deploying and maintaining forecasting models in a live
production environment.
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Experience with recent advancements in forecasting, such as foundation models
(TimesFM) or deep learning approaches.
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Experience in a demand planning, contact center, or operational workforce
management role.
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Familiarity with cloud platforms (e.g., Google Cloud Platform) and their
AI/ML services (e.g., BigQuery, Vertex AI).
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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 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.
