Lead, Data Analytics & Business Intelligence

Lead, Data Analytics & Business Intelligence - Pearson Higher Education

Description:

This role aligns to Industry Level Titles such as Product Analytics Director, or Senior Manager of Product Analytics.

Location:

US Remote

The role

At Pearson, we are the world's digital learning company with more than 24,000 employees operating in 70 countries. We lead the education technology industry in design, service, and innovation. We are committed to bringing life to a lifetime of learning and to our talented team who make it all possible. By creating effective, engaging solutions, we provide boundless opportunities for learners at every stage of their journey around the world.

We achieve this through cutting-edge technology, uncompromising service, and high-quality products that are engaging and easy to use.

You will lead product analytics for Pearson's Higher Education (HE) courseware, integrations, and content authoring portfolio - products used by millions of students and tens of thousands of instructors. Your job is not to staff a reporting function. Your job is to make sure every product team in HE is making better decisions because of data: understanding how students and instructors actually use what we build, proving which bets pay off, and surfacing the opportunities our product managers couldn't see on their own.

You will manage a small team of product analysts and partner directly with Heads of Product, PMs, designers, engineers, and learning science. You report into the HE product organization, not into a central data function - because analytics here is a product capability, not a service desk.

What "good" looks like in this role

Success in this role means setting the product analytics vision for HE and making data a practical, trusted driver of product strategy, commercial outcomes, and learning outcomes. You will lead your team and partners against these expectations:

• 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.
• 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.
• 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.
• 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.
• 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.
• Raise the bar for the analytics function. Coach and lead analysts while fostering innovation, analytical rigor, continuous improvement, and strong product partnership.

Responsibilities

• Lead the product analytics team. Manage, mentor, and grow a team of product analysts. Set the standard for analytical rigor, communication, and product partnership. M
• Drive departmental vision and strategy. Align analytics priorities with HE product goals, business strategy, and measurable learning and commercial outcomes.
• Partner with product leadership on strategy. Sit in roadmap and quarterly planning, bring the data point of view to prioritization, and push back when proposed work has no measurable outcome attached.
• Build and sustain cross-functional partnerships. Work closely with product, design, engineering, learning science, and data platform partners to maximize the impact of data across the portfolio.
• Own the HE product KPI framework. Define the small set of metrics that matter across commercial performance and learning outcomes, and make sure teams can see and use them in near-real time.
• Drive instrumentation and telemetry. Partner with engineering and data platform teams to define what we measure, where, and how, treating instrumentation as a first-class product requirement.
• Lead and scale experimentation. Build the best tooling, expectations, and cultural norms for testing, evidence-based decisions, and honest interpretation of results across HE products.
• Surface new product opportunities. Run discovery-oriented analyses across usage, outcomes, support, content interaction, and instructor behavior to identify opportunities that can change roadmaps.
• Communicate insights to executives. Translate complex analysis into clear, actionable…