Lead Data Analyst

About Target and the team

Role overview

Responsibilities

• Frame ambiguous stakeholder problems as analytical questions, measurement frameworks, data solutions, models, and decision capabilities.
• Design and deliver analytical solutions and AI capabilities for decision support, predictive insights, risk models, and reusable analytics.
• Develop analytical frameworks such as Change Risk Score, predictive alerting, and technology health signals; manage methodology, validation, monitoring, and adoption.
• Connect data across software delivery, applications, infrastructure, incidents, reliability, security, ownership, and dependencies to identify patterns and drivers.
• Evaluate AI workflows using quality measures such as accuracy and relevance; assess reliability and business impact.
• Document AI solutions, analytical methods, model assumptions, evaluation approaches, and responsible-use considerations.
• Build trusted relationships with technology and product leaders and tell data stories that influence decisions.
• Mentor analysts and raise analytical standards through reusable methods, reviews, learning forums, and iterative model building.
• Apply data protection and responsible AI practices, including privacy, bias mitigation, explainability, governance, and risk considerations.
• Monitor emerging analytics, data science, GenAI, and agentic AI methods and identify opportunities across ITDP.

Tools and analytical methods

• The role calls for large-scale dataset experience using tools such as Google Cloud BigQuery, Spark, or SQL-based warehouses, and data pipelines using Airflow or similar tools.
• Relevant work includes statistical methods, applied data science, GenAI, agentic AI, predictive analytics, and AI workflow evaluation.

Qualifications available in the provided text

• The description includes demonstrated thought leadership applying data, analytics, data science, and AI to complex technology problems.
• It calls for independent problem solving, analytical judgment, stakeholder communication, large-scale data expertise, and practical experience evaluating AI-driven analytical workflows.
• The About You/experience section is truncated after the opening experience text; further qualifications cannot be verified.