Marketing Data Science Lead

Actionable Insight: Generate compelling and actionable insights from complex, multi-source marketing data sets that directly inform channel investment, campaign design, and pipeline strategy.

Stakeholder engagement: Establish strong collaborative relationships with marketing leaders and operators across demand gen, partner, field, product marketing, and brand, delivering high-impact analytics initiatives that translate loose, evolving requirements into clear deliverables.

Attribution & Measurement: Design, build, and maintain the attribution framework for UpGuard – spanning first and multi-touch attribution and incrementality testing – and clearly communicate the trade-offs and assumptions behind each lens.

Channel & Program Analytics: Develop a deep, first-principles understanding of channel logic across paid media, organic, content, lifecycle, partner, and field, and build the metrics, models, and dashboards that let each program owner self-serve their performance.

Data Products: Partner with the data engineering team to design, construct, and maintain foundational marketing data assets – translating loose marketing 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 marketing stakeholders to provide robust self-service and conversational and agentic analytics capabilities, using design thinking principles to build user-friendly dashboards for funnel performance, channel ROI, partner sourced pipeline, and field event attribution.

Deep Dive analysis: Personally conduct thorough, hands-on, technical analysis to diagnose and solve the most significant marketing challenges – from channel saturation and diminishing returns to lead quality decay and campaign cannibalisation.

Commercial Analysis: Provide ongoing operational support to the commercial growth of the organisation, connecting marketing investment to pipeline, ARR, and payback in ways that finance and the executive team trust.

Proven Leadership: 4+ years delivering analytics solutions for Marketing teams within a high-growth SaaS company.

Marketing Domain Expertise: Strong working knowledge of channel logic across paid, organic, lifecycle, partner, and field marketing – including how each channel is planned, instrumented, and measured – and fluency in attribution methodologies (multi-touch, incrementality, and marketing mix modelling).

Partner & Field Marketing Fluency: Understanding of how partner-sourced and partner-influenced pipeline is tracked, and how field marketing programs (events, conferences) are measured against pipeline and revenue outcomes.

Requirements Translation: A demonstrated ability to take loose, ambiguous, or evolving marketing requirements and translate them into well-structured dbt models, clear metric definitions, and insights stakeholders can act on.

Consulting Background: Experience in a consulting role, successfully managing a diverse portfolio of projects across various companies and stakeholder groups.

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 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 attribution and MMM 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 marketing 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.

Data Science / AI Engineering: Proven experience as a data scientist or AI engineer, ideally within a SaaS company environment, with exposure to uplift or lead score prediction modelling, causal inference techniques, or building/evaluating the tool-calling and retrieval layers that let LLM agents query structured business data.

Marketing Tech Stack: Hands-on experience with marketing source systems such as HubSpot, Salesforce, paid media platforms (Google Ads, LinkedIn, Meta), and lifecycle tools, and an understanding of how their data shapes downstream analytics.

Financial Acumen: Experience connecting marketing analytics to FP&A processes – budget planning, payback modelling, CAC/LTV reporting, and board-level marketing narratives.