Senior Business Data Scientist, Marketing

Marketing leaders are often asked to make high-stakes investment decisions using signals that are incomplete, conflicting, or difficult to interpret. At Jobber, you will build the measurement systems, causal models, and decision frameworks that help separate true incremental impact from correlation, connect marketing investment to long-term customer value, and guide how millions of dollars are allocated. We are looking for a Senior Business Data Scientist, Marketing to join Customer Analytics and shape the quantitative foundation of Jobber’s marketing strategy. The Team Analytics is Jobber’s internal consulting and decision-support function, connecting data and business insight with teams across the organization. Customer Analytics helps Jobber understand how we acquire, monetize, serve, and retain customers—and turns that understanding into decisions that improve growth and long-term customer value. This role brings specialist Business Data Science depth to Customer Analytics, combining advanced statistical modelling, causal inference, experimentation, simulation, forecasting, and optimization with deep business context to help leaders make complex, high-value decisions. You will work closely with Customer Analytics colleagues and partners across Marketing, Finance, BI & Analytics Engineering, Data Science, Data Engineering, and other teams across Jobber. The Role Reporting to the Manager, Customer Analytics, the Senior Business Data Scientist, Marketing is a senior individual contributor who will bring advanced analytics and applied decision science to Jobber’s growth engine. Your core domain will be Marketing, with particular emphasis on performance marketing and demand generation. You will design and evolve the quantitative backbone of Jobber’s marketing measurement—helping the company understand which investments drive incremental subscription growth, how channel effectiveness changes over time, and where the next marketing dollar will generate the greatest long-term value. This is a hands‑on, technically deep role. You will build models, experiments, simulations, and measurement frameworks that influence how millions of dollars in marketing investment are allocated. You will connect marketing activity not only to immediate acquisition, but also to customer quality, revenue, payback, retention, and lifetime value. The role is focused on applied data science and decision science rather than production ML engineering. You will own the analytical work end to end—from problem formulation and methodology selection through development, validation, interpretation, and business application. You will not be responsible for MLOps, model deployment, or the engineering and operational maintenance of production systems. When a proven analytical framework would benefit from automation or productionization, you will partner with Data Science, BI & Analytics Engineering, and Data Engineering, bringing the methodological context and business logic needed to scale it effectively. Understanding the full impact of marketing investment often requires following customer outcomes beyond initial acquisition. Your work may therefore include selected questions involving conversion, channel partnerships, sales‑assisted acquisition, retention, and other downstream outcomes, always in service of better marketing and acquisition decisions. You will operate as a trusted thought partner to Marketing and Analytics leadership, independently lead ambiguous and high‑impact work, and act as a force multiplier for the broader analytics team. This is an individual contributor role: you will lead through expertise, technical ownership, influence, and mentorship rather than through direct people management. What You’ll Own Build Jobber’s Marketing Measurement System • Design and evolve an integrated marketing measurement system that brings together marketing mix modelling (MMM), attribution, incrementality testing, experimentation, forecasting, and business performance diagnostics, helping leaders reconcile different signals and understand the strengths and limitations of each approach. • Measure the contribution of directly attributable and harder‑to‑attribute investments—including paid, organic, referral, partnership, brand, upper‑funnel, and offline activity—using methods suited to each channel’s data and measurement constraints. • Develop response curves, saturation and diminishing‑returns models, spend‑efficiency analyses, and budget‑allocation scenarios that inform channel and portfolio investment decisions. • Connect marketing and channel investment to subscription revenue, CAC, payback, customer quality, LTV, retention, and long‑term business value. • Evaluate and challenge vendor, platform, and third‑party measurement outputs, including the assumptions and biases embedded in platform‑reported attribution. Apply Advanced Analytics to Growth Decisions • Apply causal‑inference and statistical techniques to distinguish true incremental impact from correlation, selection effects, seasonality, and noise. • Design and evaluate experiments and quasi‑experiments, including A/B tests, geo or matched‑market tests, synthetic‑control approaches, and other appropriate impact‑evaluation methods. • Build predictive and decision‑support models involving customer lifetime value, acquisition quality, conversion propensity, lead or customer scoring, segmentation, and downstream customer outcomes. • Develop forecasts, simulations, and scenarios that help leaders understand expected outcomes, uncertainty, risk, and trade‑offs under different investment decisions. • Translate ambiguous business questions into well‑structured analytical problems, selecting methods that are appropriate for the decision, data, timeline, and level of precision required. • Clearly communicate confidence, assumptions, methodological limitations, and what the available evidence does—and does not—support. Act as a Strategic Partner to Marketing Leaders • Partner closely with leaders and teams across Demand Generation, Performance Marketing, Brand Marketing & Communications, Marketing Operations, Finance, and adjacent analytics functions. • Help shape the marketing measurement and advanced analytics roadmap by translating stakeholder objectives into proposed analytical solutions—including models, experiments, KPIs, reporting, and decision systems—and building alignment around the capabilities and investments that will have the greatest business impact. • Proactively identify opportunities to improve marketing efficiency, channel mix, customer quality, conversion, revenue performance, and long‑term value. • Support forecasting, planning, target setting, and resource‑allocation decisions across the acquisition and revenue funnel. • Translate and present complex analytical findings in business reviews, leadership updates, planning forums, and cross‑functional working sessions, making recommendations, uncertainty, and trade‑offs clear and actionable for senior leaders. Build Scalable, AI‑Native, and Trusted Analytical Capabilities • Create reproducible, well‑documented analytical workflows with strong quality assurance, peer review, version control, and clear methodological standards. • Develop reusable models, frameworks, and decision tools that make high‑quality analysis more consistent and scalable. • Define clear analytical and data requirements, and partner with BI & Analytics Engineering and Data Engineering to strengthen the datasets, metric definitions, reporting foundations, dashboards, and self‑serve capabilities needed for advanced marketing measurement. • Partner with Data Science, BI & Analytics Engineering, and Data Engineering on ML/AI capabilities, automated experimentation, and productionization opportunities that support customer acquisition and go‑to‑market decision‑making, contributing the analytical framework, business