Lead Product Insight Analyst

About Cookpad

Cookpad develops the world’s most user‑friendly products for people who cook. The use of AI and robotics broadly falls into two approaches: replacing human actions, or extending human capability. Our focus is on using these technologies to bring out people’s strengths. Cooking affects our health, cognition, society, culture, and the environment. Our challenge is to make it possible—through our products—for anyone, anywhere, to cook at an unprecedentedly high standard.

Introduction

If you are someone who…

• finds yourself in a situation where goals are set upfront as numbers or initiatives, with little room to step back and ask what problem you are really trying to solve.
• is expected to simply deliver requested analyses, with little recognition for shaping the question yourself.
• rarely gets the business and leadership context, and cannot connect the numbers to the decisions they are meant to support.
• is told that if something cannot be measured easily, it does not count—so what you believe truly matters never makes it into the metrics.
• raises misalignments or blockers, only to have them brushed aside with, “Not right now.”

If that feels stifling, you might be exactly who Cookpad is looking for—someone who can take that unease on as part of the job, and turn it into progress.

Overview

At Cookpad, every project follows a six‑week cycle of setting goals, executing, and holding a retrospective. This role is for a data analyst who understands the essence of the goals we aim to achieve and the problems we are trying to solve, designs metrics that show whether we are genuinely moving in the right direction, and proactively identifies issues and signals in the data that may be blocking progress.

In a short six‑week cycle, the key question is whether the team is operating based on the right metrics. You will help the organisation improve how accurately it can answer that question.

Mission

• Understand the essence of the problems we are trying to solve.
• Design meaningful metrics (including proxies) that are directly tied to the goals.
• Uncover key insights and blockers from data, and connect them to decision‑making.

Key Responsibilities
1. Clarifying the Essence of Problems and Goals

• For each project goal: what problem are we trying to solve? What would need to change for us to say we have made progress?
• You will re‑examine and clarify these points.
• You will connect leadership intent, product value, and user behaviour, and make the underlying problem structure explicit in clear language.
• Rather than focusing on goal‑setting itself, you will focus on ensuring the meaning of the goals is correctly understood.

2. Designing Goal‑Linked Metrics and Proxy Metrics

• Design metrics to assess progress towards achieving the goals.
• Even when the goal cannot be measured directly, define the closest meaningful alternative (a proxy metric).
• Keep only the metrics that steer the team’s behaviour in the right direction—not metrics chosen simply because they are easy to measure.

3. Extracting Insights from Data

• Using behavioural logs and event logs, identify factors that may be influencing progress towards achieving the goals, and surface clues to the next questions the team should test.
• Present analysis results as actionable insights that inform the next decisions and actions.

4. Proactively Identifying Blockers and Issues

• Go beyond requested analyses to proactively identify signals that may be getting in the way of achieving the goals.
• Before the numbers break down in a visible way, detect and raise early signals, such as unexpected patterns, bias, and shifts in behaviour.

5. Sharing Insights in a Way That Supports Decision‑Making

• Instead of producing reports and dashboards in volume, share insights in a way that makes it clear what to look at and how to make a decision.
• Keep decision‑making simple, without introducing unnecessary discussion points.

Qualifications

• Experience moving back and forth between leadership‑level context and feature‑level detail to understand problems.
• Business and leadership‑level objectives and strategy.
• Product‑ and feature‑level challenges.
• Real user behaviour data. While moving between these layers: “How does this…