Business Intelligence Manager, Operations Analytics

Prudential’s purpose is to be partners for every life and protectors for every future. Our purpose encourages everything we do by creating a culture in which diversity is celebrated and inclusion assured, for our people, customers, and partners. We provide a platform for our people to do their best work and make an impact to the business, and we support our people’s career ambitions. We pledge to make Prudential a place where you can Connect, Grow, and Succeed.

Job Profile Summary

We’re seeking a strategic and hands-on Operations Analytics Manager to uncover actionable insights across our operations value chain. This role is ideal for someone who thrives at the intersection of data science, operational strategy, and business transformation - someone who can frame the right problem, explore the data landscape, and drive decisions that matter.

Key Responsibilities

Frame the problem: Translate business pain points into structured problem statements using issue trees

Explore the data: Conduct exploratory data analysis (EDA) to identify root causes, patterns, and anomalies across operational processes

Hypothesis-driven analytics: Formulate and test hypotheses using statistical and scripting methods to validate root causes and solution levers

Insight generation: Translate complex data into clear, actionable insights for senior stakeholders across underwriting, claims, and operations

Action design: Recommend and track interventions based on data-driven findings

Data handling: Use SQL and Python to extract, clean, and join datasets from multiple sources into tidy, analysis-ready formats

Framework development: Build repeatable analytics frameworks and dashboards to monitor performance, quality, and risk signals

Partner cross-functionally: Collaborate with product, tech, and frontline teams to ensure insights are embedded into decision-making and design

Qualifications & Skills

5+ years in data analytics, operations strategy, or consulting (insurance or financial services preferred)

Strong command of SQL and Python for data extraction, transformation, and analysis

Experience with EDA, hypothesis testing, and root cause analysis

Familiarity with issue tree frameworks

Ability to translate data into business impact - clear communicator with executive presence

Experience working with large, messy, or cross-functional datasets

Exposure to insurance operations, claims workflows, or underwriting systems