Team Manager, Data Analytics & Business Intelligence

1. Understand actual behavior. Instrument products so we can see what students and instructors do, not what they say. Close the gap between stated needs and revealed behavior. 

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2. Measure business and learning performance. Define and own the KPI trees for HE products — both commercial (activation, retention, revenue per learner) and learning (engagement-to-outcome conversion, time-on-task efficiency, assignment completion, demonstrable mastery gains). At Pearson, a product that drives revenue but not learning outcomes is a failure. Your metrics must reflect both. 

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2. Prove which ideas work. Stand up and scale the experimentation practice across HE — A/B tests, holdouts, live-data prototypes. Coach PMs on test design, sample sizing, and reading results honestly (including the unwelcome ones). Kill bad ideas faster. 

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2. Inform product decisions. Replace opinion-driven debates with evidence. When leadership, PMs, or stakeholders disagree, you produce the analysis that resolves the question — or makes clear the question can't be resolved with the data we have, and what we'd need to collect. 

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2. Inspire new product opportunities. Mine our data — usage, outcomes, support, content interaction, instructor behavior — to surface opportunities no one asked you for. Some of the most valuable product work in this org should be initiated by your team, not handed to it. 

Responsibilities 

• Lead the product analytics team. Manage, coach, and grow a team of product analysts. Set the standard for analytical rigor, communication, and product partnership. Make every analyst on your team a stronger product thinker, not just a stronger SQL writer. 

• Partner with product leadership on strategy. Sit in roadmap and quarterly planning. Bring the data point of view to prioritization. Push back when proposed work has no measurable outcome attached. 

• Own the HE product KPI framework. Define the small set of metrics that matter — across commercial performance and learning outcomes — and make sure every team can see theirs in near-real time. 

• Drive instrumentation and telemetry. Work with engineering and data platform teams to define what we measure, where, and how. Treat instrumentation as a first-class product requirement, not an afterthought. 

• Lead and scale experimentation. Build the muscle, the tooling expectations, and the cultural norms for testing across HE products. 

• Surface opportunities. Run regular discovery-oriented analyses across the portfolio. Bring forward "we should look at this" insights that change roadmaps. 

• Communicate to executives. Translate analysis into a narrative HE and Pearson leadership can act on. Less dashboard, more decision. 

• Govern data quality. Hold the line on definitions, lineage, and trustworthiness. A wrong number that ships to a VP is a tax on every future decision. 

What we're looking for 

• 8+ years in product analytics, with at least 2–3 years managing analysts. You've done the work and you've built the people who do the work. 

• A demonstrable bias toward outcomes over outputs. You can point to product decisions, experiments, or roadmap changes that happened because of analysis you led — and to the resulting learner or business impact. 

• Fluency in the product operating model. You've worked directly with empowered product teams (or you've helped create them) and you understand the difference between feature teams and product teams. 

• Strong experimentation chops. You can design a test, size it, read it, and tell a PM when not to run one. 

• Technical depth: SQL (BigQuery, Snowflake), product analytics tools (Mixpanel or equivalent), Tableau, Python a plus. We expect you to be hands-on — you should still be able to run the analysis yourself, even when you don't have to. 

• Sharp communication. You can hold your own in a room of senior product and engineering leaders and translate ambiguity into clear questions and clearer answers. 

• Strongly preferred: experience in education, edtech, or any domain where learning, behavior change, or skill acquisition is the core user outcome. You should care that what we build actually improves learning outcomes.