Business Data Scientist, gUP

The gTech Users and Products (gUP) Analytics Science and Forecasting team serves as the strategic advisory engine for gUP executive leadership and Operations. By applying solutions, we answer the most critical questions from our leaders, uncovering productivity levers required to optimize operations, drive operational agility, and scale gUP.Google creates products and services that make the world a better place, and gTech’s role is to help bring them to life. Our teams of trusted advisors support customers globally. Our solutions are rooted in our technical skill, product expertise, and a thorough understanding of our customers’ complex needs. Whether the answer is a bespoke solution to solve a unique problem, or a new tool that can scale across Google, everything we do aims to ensure our customers benefit from the full potential of Google products.

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Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

Ireland: €90000 - €92000 (EUR) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Develop, tune, and review time-series forecasting models (e.g., ARIMA, Fargo) using R, Python, and SQL to accurately predict 1:1 support volumes and handle times.

Manage a growing internal operations hub handling daily forecasting execution. Take technical ownership of pipeline integrity by troubleshooting complex errors, validating system configurations, and resolving reporting issues when the standard workflows require advanced triage.

Monitor model health and forecast accuracy. Conduct root-cause analysis on variance and implement methodology adjustments to ensure high-fidelity predictions at scale.

Partner across various business and operational teams to gather forecasting inputs and deliver actionable outputs used for critical staffing and capacity decisions.

Create and leverage agentic and LLM-based solutions to automate and enhance support operations and forecasting pipelines.

Minimum qualifications:

Bachelor's degree in Statistics, Mathematics, Data Science, Economics, Operations Research, a related quantitative field, or equivalent practical experience.

3 years of experience using analytics to solve product or business problems, coding (e.g., Python, R), querying databases (e,g., SQL), or statistical analysis.

Experience in cross-functional program management, project management, or operational process execution; or managing vendor teams, operations hubs, or leading distributed operational workflows.

Experience with forecasting demand or volume in an operations, contact center, workforce management, or supply chain context, or with support operations metrics (e.g., SLA, AHT, Shrinkage) and capacity planning and staffing methodologies (e.g., Erlang C).

Preferred qualifications:

Master's degree in Statistics, Mathematics, Data Science, Economics, Operations Research, a related quantitative field, or equivalent practical experience.

Experience with advanced time-series forecasting methods (e.g., ARIMA, exponential smoothing) applied to complex operational datasets

Experience managing vendor teams, operations hubs, or leading distributed operational workflows.

Experience building or leveraging agentic and Large Language Model (LLM)-based solutions to automate and scale analytical workflows.