Product Data Science Lead

UpGuard; United States; Remote; USD 110,000 - 150,000; Full time

Job summary

UpGuard is seeking an experienced Data Scientist to own product analytics end-to-end. You will quantify product performance, illuminate engagement patterns, and translate complex product questions into durable data models and insights.

You will partner with Product, Operations, Design, and Engineering to shape KPIs and milestones, helping drive adoption across the platform while bridging Product and Sales/CS.

Qualifications

• Experience turning ambiguous product questions into data models and insights.
• Ability to quantify product performance and engagement across platforms.
• Proven track record delivering actionable analytics to product and sales teams.

Responsibilities

• Generate actionable insights from multi-source product and usage data that inform roadmap and engagement strategy.
• Collaborate with Product, Operations, Design, Engineering, and Success to translate requirements into deliverables.
• Define and maintain product KPIs and milestones from activation through ongoing engagement.

Skills

Data science

Product analytics

SQL

Python

Tools

Python (tool)

Job description

Who are we?

At UpGuard, we are replacing manual security bottlenecks with AI-driven precision. Fresh off a US$75M Series C, we are scaling our infrastructure to process 100 billion risk signals daily. This isn’t just growth; it’s a total reimagining of how the world manages cyber risk.

We build the Cyber Risk Posture Management (CRPM) platform that security teams actually love. By integrating security ratings, threat intel, and agentic AI, we empower organisations to stay ahead of an ever evolving attack surface.

We aren’t just building another tool; we’re defining a category. We provide the autonomy to ship world-class technology and the resources to do it at a global scale.

Our product is central to that next chapter. We're investing heavily in new products and features, with an exciting roadmap to keep building out UpGuard's best-in-class platform. In an era where third-party risk is more complex than ever, we maintain a highly collaborative, consultative culture that puts the customer's security posture above all else.

Where does this role fit in?

As UpGuard continues its rapid growth trajectory, we are seeking an experienced data scientist to support our Product team. Reporting to the Director of Analytics, this critical role is responsible for driving significant business value by quantifying product performance, illuminating what good engagement and adoption look like across the platform, and translating ambiguous product questions into durable data models and insights. This role will partner closely with Product Managers, Operations, Design, and Engineering to help the Product team define KPIs and milestones, understand what's working, and see how usage translates into retention and growth — while also acting as the interface between Product and Sales/CS to drive adoption of what's being built.

This is an autonomous, lead role: you'll own product analytics end-to-end, supporting UpGuard's product portfolio across both established and emerging product lines.

This is a ground-up build: the models and metric definitions this person creates will form the governed semantic layer that both humans and AI/agentic analytics tools query, so clarity and rigor in the modelling layer compounds directly into AI-enabled self-service.

What will you do?

Actionable Insight: Generate compelling and actionable insights from complex, multi-source product and usage data sets that directly inform roadmap prioritisation, feature investment, and engagement strategy.

Stakeholder Engagement: Establish strong collaborative relationships with Product Managers, Operations, Design, Engineering and Success, delivering high-impact analytics initiatives that translate loose, evolving requirements into clear deliverables.

KPI Milestone Definition: Design, define, and maintain the product KPIs and engagement milestones for UpGuard – from activation and onboarding through to what "good" ongoing engagement looks like – and clearly communicate the trade-offs and assum