Job Description

Manager, Data Analytics & Business Intelligence - Pearson Higher Education

Location: Hoboken - Hybrid

The role

You will lead product analytics for Pearson's Higher Education 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

We expect strong product analytics leaders to drive five uses of data. You will lead your team and your product partners against all five:

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.

1. 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.

1. 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.

1. 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.

1. 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.

Compensation at Pearson is influenced by a wide array of factors including but not limited to skill set, level of experience, and specific location. As required by the California, Colorado, Hawaii, Illinois, Maryland, Minnesota, New Jersey, New York State, New York City, Vermont, Washington State, and Washington DC laws, the pay range for this position is as follows:

The minimum full-time salary range is between $155,000 - $175,000.

This position is eligible to participate in an annual incentive program, and information on benefits offered is here.

Applications will be accepted through May 22nd. This window may be extended depending on business needs.