[![Image]() Meet Jobicy Copilot — free AI autofill for job applications + remote job alerts ›](#) [All remote jobs](https://jobicy.com/jobs.md)Open role[![UpGuard logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/06/474a12c7-221.png)](https://jobicy.com/company/upguard.md)Remote opportunity at[UpGuard](https://jobicy.com/company/upguard.md) # Product Data Science Lead Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/upguard.md)Share14 Sep 2026Published44Listing views3Application actions14 Oct 2026Apply before Opportunity details ## About this role. AI SummaryUpGuard seeks a Product Data Science Lead to own product analytics across established and emerging SaaS product lines. The role will define activation, engagement, adoption, retention, and commercial-health metrics; develop governed dbt models and semantic-layer assets; and produce self-service BI capabilities. This person will partner closely with Product, Engineering, Design, Operations, Sales, and Customer Success to turn ambiguous questions into trusted insights and adoption actions. It is a senior, hands-on analytics leadership role requiring strong statistical judgment, modern data-stack expertise, and influential stakeholder communication. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThe role combines end-to-end ownership of product analytics with hands-on data modeling, complex multi-source analysis, and cross-functional influence. Success depends on building durable metric foundations while supporting a fast-growing product organization with evolving requirements. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job medianHighly competitive$185,000US market range$160k–$210k0$231k AI insightNo base salary or pay range is disclosed; the only dollar amount is a $1,500 annual learning and development allowance, which is not salary. Estimated US-market annual base salary for a lead-level product data science/analytics role is $160,000-$210,000 USD, with an estimated median of $185,000 USD; actual compensation may vary by location, equity, and total-rewards structure. ## Core skills Skills and capabilities most closely associated with this opportunity. [Product analytics](https://jobicy.com/jobs?search_keywords=Product%20analytics.md)[Data science](https://jobicy.com/jobs?search_keywords=Data%20science.md)[dbt](https://jobicy.com/jobs?search_keywords=dbt.md)[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[Business intelligence](https://jobicy.com/jobs?search_keywords=Business%20intelligence.md)[KPI design](https://jobicy.com/jobs?search_keywords=KPI%20design.md)[SaaS metrics](https://jobicy.com/jobs?search_keywords=SaaS%20metrics.md)[Data modeling](https://jobicy.com/jobs?search_keywords=Data%20modeling.md)[Retention analysis](https://jobicy.com/jobs?search_keywords=Retention%20analysis.md)[Stakeholder management](https://jobicy.com/jobs?search_keywords=Stakeholder%20management.md) Sample interview questionsHow would you define a north-star metric and supporting KPI framework for a multi-product B2B SaaS platform?I would begin with the customer outcome the platform is intended to create, then identify a measurable behavior that reliably indicates customers are realizing that outcome. I would pair the north-star metric with leading indicators across onboarding, activation, feature adoption, engagement quality, and account health, plus lagging indicators such as retention and expansion. Each metric would have an explicit population, time window, calculation logic, owner, and known limitations documented in the semantic layer. Describe how you would investigate a sudden drop in product activation. I would first validate that the decline is real by checking instrumentation changes, data freshness, denominator shifts, and cohort sizes. Next, I would segment the funnel by acquisition source, customer segment, product configuration, geography, platform, and release version to isolate where behavior changed. I would combine quantitative funnel analysis with qualitative input from Product, Support, and Customer Success, then recommend prioritized experiments and define success criteria before implementation. What is your approach to building trusted dbt models and a governed metrics layer? I use layered models that separate raw ingestion, standardized entities, and business-ready marts, with tests for uniqueness, referential integrity, freshness, and accepted values. I define events, accounts, users, product entitlements, and lifecycle milestones consistently so downstream metrics share common logic. Documentation, version control, code review, lineage visibility, and clear metric ownership are essential so both people and AI tools can query the data safely and consistently. How would you connect product-usage signals to renewal and expansion opportunities? I would work with Sales and Customer Success to identify behaviors historically associated with successful renewals, expansion, and churn, while controlling for customer size, tenure, and plan type. I would build transparent health indicators using product usage, breadth of adoption, time-to-value, engagement trends, and support or commercial context. The output should be actionable: clear account segments, reason codes, confidence levels, and recommended adoption plays rather than an opaque score alone. How do you communicate statistical uncertainty to non-technical stakeholders making roadmap decisions? I focus on the decision rather than statistical terminology alone. I explain the observed effect, practical impact, confidence interval or uncertainty range, sample size, and key assumptions in plain language, then state what decision is justified now and what evidence is still needed. When evidence is weak, I recommend a low-risk experiment or additional measurement rather than presenting noisy results as certainty. ### 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, and translate data into strategic insights that drive tangible business value. * Communication & Influence: Excellent communication (verbal and written) skills, with the ability to articulate complex analytical concepts – including KPI definitions and engagement trade-offs – to both technical and non-technical audiences and influence decision-making. * Strategic & Analytical Thinking: Highly analytical and strategic mindset, with a proven track record of developing and executing data strategies that align with product and business objectives. * Data Development: Directly engage in the creation of fundamental data and Business Intelligence (BI) infrastructure and assets, including hands-on dbt model authorship. * AI Fluency: Fluency leveraging AI/LLM tools to accelerate analysis, code development, and documentation. ### What will give you an edge? * Data Science / AI Engineering: Proven experience as a data scientist or AI engineer, ideally within a SaaS company environment, with exposure ing, engagement scoring, causal inference techniques, or building/evaluating the tool-calling and retrieval layers that let LLM agents query structured business data. * Product Tech Stack: Hands-on experience with product analytics and CS tools such as Segment, Mixpanel, Amplitude, HubSpot, and Salesforce, and an understanding of how their data shapes downstream analytics. * SaaS Metrics Fluency: Strong grasp of core B2B SaaS metrics – NRR/GRR, activation and time-to-value, product-qualified leads, expansion and churn drivers – and how to connect product engagement data to these outcomes. ### What’s in it for you? Monthly Lifestyle subsidy: Use this for financial, physical, and mental well-being * WFH set-up allowance: To ensure you have the right environment to work in, we will help you get set up within your first 3 months at UpGuard * $1500 USD annual Learning & Development allowance: To support your career development, all team members will be able to expense development opportunities against this allowance * Annual leave: PTO plus two additional UpGuardian leave days to give you time to recharge your batteries. * 18 weeks paid Parental Leave: Irrespective of parenting role * Personal Leave Allowance: This includes sick & carer’s leave * Fully remote working environment: While we have physical offices in Sydney & Hobart, we do not mandate compulsory attendance * Top-spec hardware: All team members will be provided with top-spec laptops for their role * Generative AI subsidy: UpGuard provides paid subscriptions for all team members to access generative AI tools to support their work * Stock Options: Share in UpGuard’s growth and long-term success. UpGuard is a Certified Great Place to Work® in the US, Australia, UK and India, establishing its position as a leading global technology employer. 99% of team members agree that UpGuard is a great place to work! Apply now to find out why! As an Equal Employment Opportunity and Affirmative Action Employer, qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender perception or identity, national origin, age, marital status, protected veteran status, or disability status. Please Note: Not all roles can be performed from the United States. Please check your specific job listing to confirm its advertised location. If the role you are applying for is listed as based in the US, we are currently only able to support hiring in the following locations: CA, CO, FL, IL, LA, MA, MD, MO, OR, PA, TX, WA, and DC. Before starting work with us, you will need to undertake a national police history check and reference checks. Also, please note that at this time, we cannot support candidates requiring visa sponsorship or relocation. Show more [Apply now >](https://jobicy.com/jobs/153276-product-data-science-lead.md) > Annual salary information is not provided for this position. 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