Data Scientist in United

This role will work across both Data Science and Data Engineering, with a focus on analyzing operational data, developing performance benchmarks, maintaining dashboards, and troubleshooting vendor-facing data challenges., * Analyze operational datasets to identify trends, performance gaps, anomalies, and opportunities for improvement. \n

• Support measurement and benchmarking initiatives, including AHT and task-level performance benchmarks. \n

• Build, maintain, and analyze dashboards used to monitor vendor and operational performance. \n

• Partner with vendor managers and cross-functional stakeholders to translate operational questions into data-driven insights and recommendations. \n

• Establish, validate, and publish new operational benchmarks. \n

• Troubleshoot vendor-facing data challenges, including missing or inconsistent data, system discrepancies, permissions/access issues, and potential bugs. \n

• Support data engineering needs related to vendor data and operational reporting. \n

• Proactively identify additional issues, risks, and opportunities and independently drive work toward resolution. \n

• Leverage AI-enabled tools where appropriate while maintaining strong analytical judgment and understanding of the underlying data. \n

• 3-5+ years of relevant industry experience in Data Science, Data Analytics, Analytics Engineering, Data Engineering, or a related field. \n

• Strong SQL and data analysis skills, including independently querying, manipulating, validating, and troubleshooting complex datasets. \n

• Dashboarding and operational measurement experience, including analyzing performance metrics and developing data-driven benchmarks. \n

• Working knowledge of data engineering concepts, with the ability to investigate missing or inconsistent data, permissions/access challenges, discrepancies, and potential bugs. \n

• Highly proactive, curious, and independent problem solver who can take an ambiguous request, determine what needs to be investigated, uncover additional problems or opportunities, and drive work forward with limited direction. \n, * Experience supporting scaled operations, vendor operations, workforce planning, content moderation, or other large-scale operational environments. \n

• Experience partnering directly with vendor managers or external-facing operational teams. \n

• Experience working in an environment where Data Science, Analytics, and Data Engineering responsibilities overlap. \n

• Experience using AI-enabled tools to improve analytical workflows while maintaining independent reasoning and validation. \n

• Experience within a large, complex technology or product organization. \n