Business Data Scientist, gUP

• 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.

• 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).