Product Data Science Lead

About the company

UpGuard provides an AI-powered Cyber Risk Posture Management platform that enables organizations to monitor and remediate vendor, attack-surface, and workforce security risks.

About this role

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 assumptions behind each definition. - Product & Feature Analytics: Develop a deep, first-principles understanding of the product funnel across onboarding, activation, feature adoption, and retention, and build the metrics, models, and dashboards that let PMs and feature owners self-serve their performance. - Data Products: Partner with the data engineering team to design, construct, and maintain foundational product and usage data assets – translating loose product requirements into well-specified dbt models and a governed semantic/metrics layer that both humans and AI agents can reliably query and traverse. - Business Intelligence: Partner strategically with Product stakeholders to provide robust self-service and conversational and agentic analytics capabilities, using design thinking principles to build user-friendly dashboards for engagement health, feature adoption, and activation performance. - Deep Dive Analysis: Personally conduct thorough, hands-on, technical analysis to diagnose and solve the most significant product challenges – from onboarding drop-off and feature underperformance to engagement decay and churn risk. - Commercial & Adoption Analysis: Act as the connective tissue between Product and Sales/CS, translating product usage and engagement signals into adoption plays, health scores, and expansion/renewal risk signals in ways that Sales, CS, and the executive team trust. WHAT WILL YOU BRING? - Proven Experience: 4+ years delivering analytics solutions for Product teams within a high-growth SaaS company. - Product Domain Knowledge: Strong working knowledge of product analytics – activation, engagement, feature adoption, and retention – including how KPIs and milestones are defined, instrumented, and measured, and fluency in translating engagement data into a north star metrics framework. - Statistical Rigor: Comfort with core statistical reasoning — significance, confidence, sample size, variance, and the risk of misleading metrics from small or noisy cohorts — applied to keep KPI definitions and dashboards honest. - Sales & CS Fluency: Understanding of how product usage and engagement signals are consumed by Sales and Customer Success – health scores, expansion signals, renewal risk, and adoption plays – and how to package product data for those audiences. - Requirements Translation: A demonstrated ability to take loose, ambiguous, or evolving product requirements from PMs, Operations, Design, and Engineering and translate them into well-structured dbt models, clear metric definitions, and insights stakeholders can act on. - Data Infrastructure Expertise: Strong understanding of modern data infrastructure (e.g., cloud data warehouses like BigQuery; ETL/ELT tools; dbt model development; modern data visualisation tools like ThoughtSpot, Omni, Looker). - Exceptional Business Acumen: Ability to quickly understand complex product and business problems, identify key performance indicators