3 weeks ago

Product Data Science Lead

UpGuard

Melbourne

3 weeks ago

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UpGuard is seeking an experienced Product Data Science Lead to drive product analytics, define KPIs, and build data models for their Cyber Risk Posture Management platform. This autonomous role partners with Product, Sales, and CS teams to translate product usage into actionable insights and adoption strategies. Requires 4+ years of analytics experience in high-growth SaaS, strong product domain knowledge, and fluency in modern data infrastructure.

Core AIRemoteFull-timeLeadProduct AnalyticsKPI Definition

Product Data Science Lead

Apply

Core AIRemoteFull-timeLeadProduct Analytics

Salary

Not specified

Work Location

Melbourne, Victoria, Australia, AU

Work Model

Remote

Experience Required

4 years

Employment Type

Full-time

Experience Level

Lead

Core Qualifications

Technical (Must-have)

Product AnalyticsKPI DefinitiondbtBigQueryETL/ELTData VisualizationThoughtSpotOmniLookerStatistical AnalysisSQLData ModelingBusiness IntelligenceAI/LLM ToolsCommunication

Soft Skills

CommunicationInfluenceStrategic ThinkingAnalytical ThinkingCollaborationBusiness Acumen

Preferred Qualifications

Technical (Nice-to-have)

Data ScienceAI EngineeringCausal InferenceLLM AgentsSegmentMixpanelAmplitudeHubSpotSalesforceSaaS MetricsNRRGRRProduct-Qualified Leads

Key Responsibilities

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

Product AnalyticsData ScienceSaaSCyber SecurityRemoteLeaddbtBigQueryAIBusiness Intelligence

Key Responsibilities

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

•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.

•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.

•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.

•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.

•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.

•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.

•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.