Senior Business Data Scientist, Marketing

Jobber•Toronto, ON

8d•CA$151,200 - CA$204,500•Hybrid

About The Position

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.

Requirements

Significant hands-on experience in digital or performance marketing analytics, marketing science, growth measurement, or a closely related field, ideally in a SaaS, subscription, marketplace, or similarly complex business. Your experience includes methodological thought leadership, consultative influence, or both.
Broad, practitioner-level knowledge across the advanced marketing measurement ecosystem, including: marketing mix modelling; multi-touch attribution and other attribution methods; incrementality measurement and experimentation; causal inference; forecasting and scenario modelling; budget allocation and optimization; and customer lifetime value and acquisition-quality measurement.
Meaningful applied experience spanning MMM, attribution, and incrementality, with deeper specialization in one or more of these areas. Your incrementality experience includes designing or evaluating experiments and quasi-experiments and selecting appropriate approaches based on the business question, available data, and practical constraints.
Experience building, materially improving, or operating an integrated marketing measurement system that connects multiple methodologies to real investment decisions.
Hands-on experience measuring at least one harder-to-attribute area—such as brand, upper-funnel, offline, referral, or partnership activity—and selecting methods suited to incomplete, indirect, or imperfect signals.
The judgment to evaluate, select, connect, and challenge different measurement methods—including vendor, platform, and third-party outputs—and to reconcile conflicting evidence rather than treating any one model or reported result as definitive.
Expert-level SQL skills and the ability to work confidently across complex relational data structures, construct analytical datasets, and validate logic.
Strong proficiency in Python or R for statistical modelling, causal analysis, forecasting, simulation, optimization, and analytical workflow development.
Strong statistical judgment, including comfort with uncertainty, bias, variance, confidence intervals, effect sizes, seasonality, model validation, and the practical limitations of observational data.
Familiarity with modern cloud data and business-intelligence platforms such as Snowflake, dbt, Tableau, Looker, or equivalent tools.
Experience making analytical work reusable and repeatable through modular code, version control, documentation, quality assurance, and scalable analytical workflows.
Familiarity with paid-media, ad-platform, CRM, lifecycle, web, revenue-funnel, and customer data, including platform-specific constraints associated with Google Ads, Meta, and programmatic advertising, as well as the privacy, tracking, and signal-loss changes affecting modern marketing measurement.
A strong understanding of SaaS and subscription economics, including CAC, LTV, payback, conversion, MRR, ARR, NRR, churn, retention, customer quality, and long-term value.
Strong business acumen and the ability to connect analytical work to profitable growth, investment allocation, and company-level outcomes.
Excellent stakeholder-management and communication skills, including the ability to influence senior leaders and turn technically complex work into clear, decision-ready recommendations.
Practical fluency with AI-assisted analytical and development workflows, including using AI to accelerate coding, model iteration, research, quality assurance, documentation, and communication; independently validating outputs; and identifying where AI, automation, or self-serve capabilities can make analytical work more scalable.
A track record of operating independently in ambiguous environments and balancing methodological rigour with speed and practical business needs.
Experience mentoring analysts or data scientists and raising the technical and analytical bar for colleagues.

Nice To Haves

Experience with MMM libraries or statistical frameworks such as Meridian, Robyn, PyMC-Marketing, or comparable Bayesian modelling tools.
Deeper expertise in one or more specialized causal-inference approaches, such as geo experimentation, matched-market methods, synthetic control, uplift modelling, propensity-score methods, or regression discontinuity.
Deep experience across multiple harder-to-attribute investment areas, such as brand, upper-funnel, offline, referral, or partnership activity.
Experience developing maintained analytical applications, automated workflows, self-serve decision tools, or analytical frameworks that were subsequently productionized.
Experience working in marketing environments with complex channel portfolios, international markets, or multi-product acquisition strategies.

Responsibilities

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 logic, requirements, and validation standards needed to scale them responsibly.
Design analytical workflows that use AI across the full analytical lifecycle—from research and coding through model iteration, validation, documentation, and communication. Identify recurring work that should be standardized, automated, or enabled through self-serve capabilities, while maintaining reproducibility, appropriate controls, and clear human ownership of methodological and analytical quality.
Stay current on developments and industry best practices in marketing science, causal inference, measurement, forecasting, optimization, and applied decision science, and translate relevant advances into practical improvements at Jobber.
Lead as a Senior Individual Contributor: Independently own complex, ambiguous, and high-impact work from initial problem framing through modelling, interpretation, recommendation, and business adoption.
Set a high standard for statistical rigour, analytical judgment, documentation, and decision relevance.
Provide technical guidance, peer review, and mentorship to members of the Marketing and Customer Analytics teams.
Help colleagues and stakeholders select appropriate analytical methods and understand the implications and limitations of the resulting evidence.
Contribute to the development of Business Data Science practices, reusable methods, and technical standards within Customer Analytics.
Lead through influence and expertise, building trust with senior stakeholders and helping teams make better decisions without relying on formal authority.