Senior Business Data Scientist, Marketing

Jobber•Toronto, ON

5d•CA$151,200 - CA$204,500•Hybrid

About The Position

We are seeking a passionate Senior Business Data Scientist, Marketing to join our Analytics team. This role is part of the Strategy & Analytics department, functioning as an internal consulting team that bridges data and business insights with the rest of the organization. The Senior Business Data Scientist, Marketing will serve as a senior analytics partner for key Customer Analytics domains, collaborating with business leaders and cross-functional teams to deepen the understanding of customer behavior, business performance, growth opportunities, and drivers of long-term customer value. The scope of work may include acquisition, onboarding, lifecycle engagement, product adoption, monetization, retention, expansion, customer success, and other strategic customer domains. The role involves leading high-impact analytical work across the customer journey, including funnel performance, segmentation, conversion, engagement, product usage, retention, expansion, operational effectiveness, and long-term value. This is an individual contributor leadership role focused on influencing, owning business domains, shaping analytical roadmaps, mentoring analysts, managing stakeholder relationships, and ensuring analytics translate into better decisions. The ideal candidate possesses strong business acumen, deep analytical expertise, and excellent communication skills, with the ability to transform ambiguous business questions into structured analytical approaches and clear recommendations.

Requirements

Expert-level SQL skills, with the ability to work efficiently across complex relational data structures and validate analytical logic with confidence.
Strong experience in B2B SaaS, marketplace, fintech, revenue, growth, product, customer, or go-to-market analytics.
A deep understanding of SaaS business models and customer journey metrics, such as funnel performance, conversion, activation, engagement, retention, expansion, monetization, customer quality, and customer value.
Proficiency with BI and data visualization tools such as Tableau, with a focus on clear, compelling, and actionable reporting and narratives.
Demonstrated experience with foundational and advanced analytics techniques, including performance measurement, exploratory analysis, impact evaluation, experimentation, forecasting, scenario analysis, simulation modelling, and predictive analytics.
Strong business acumen and strategic judgment, with the ability to connect analytical work to company-level growth, efficiency, and customer outcomes.
Excellent stakeholder management skills, with the ability to build trust, clarify ambiguous asks, influence priorities, and guide leaders toward better decisions.
A passion for storytelling through data, with the ability to distill complex analyses into simple, influential insights.
Practical fluency with AI-assisted analytics workflows, with the judgment to use AI responsibly, validate outputs, and own the quality of the final work.
Intellectual curiosity, creativity, and adaptability in solving open-ended business problems in a fast-moving environment.
Demonstrated ability to mentor analysts, lead through influence, and operate as a senior individual contributor owning one or more business domains.
Proven ability to thrive under pressure, navigate ambiguity, and bring structure to complex or competing demands.
Be proactive and resourceful, with a bias for action. Comfortable navigating ambiguity, solving conceptual problems, corralling resources, and delivering results independently.
Communicate with clarity and confidence. Actively listen, empathize with stakeholders, and translate complex concepts into simple, actionable insights.
Care deeply about quality. Value strong analytical foundations, thoughtful QA, documentation, peer review, and reproducible work.
Be comfortable making trade-offs visible. Know how to focus on the highest-impact work while helping stakeholders understand what must be deprioritized.
Be excited about the future of analytics. See AI, automation, and self-serve not just as productivity tools, but as opportunities to redesign how analytics teams create impact.
Lead without authority. Influence senior stakeholders, guide peers, mentor analysts, and move important work forward without relying on formal people management authority.
Thrive in a fast-paced and evolving environment. Adapt quickly, embrace change, and stay focused even when yesterday’s playbook no longer applies.

Nice To Haves

Experience with Python, dbt, Snowflake, Salesforce or other CRM/customer systems, data modelling, and productionized analytics workflows is a strong asset.

Responsibilities

Lead strategic customer analytics and insight generation.
Act as a strategic thought partner and internal consultant to stakeholders, helping them clarify business questions, evaluate opportunities, measure performance, and make better decisions.
Collaborate closely with teams across the organization, including Revenue Operations, Strategy, Marketing Analytics, Product & Fintech Analytics, BI & Analytics Engineering, Data Science, and go-to-market or customer-facing teams.
Lead deep-dive analyses across the customer journey, including acquisition, onboarding, engagement, product adoption, monetization, retention, expansion, customer quality, and long-term customer value.
Define, refine, and govern key performance indicators (KPIs) for assigned domains, including performance, efficiency, customer quality, customer outcomes, and downstream business impact.
Translate complex business questions into clear analytical plans, decision frameworks, and actionable recommendations.
Evaluate the impact of strategic initiatives, operational changes, go-to-market motions, customer programs, product or lifecycle initiatives, and other business priorities.
Help leaders understand not just what happened, but why it happened, what it means, and what actions should be taken.
Build a strong analytical understanding of assigned domains, identifying opportunities to improve growth, efficiency, prioritization, customer experience, customer quality, and long-term value.
Support strategic planning, forecasting, target setting, business cases, and resource allocation decisions.
Partner with cross-functional teams and business leaders to improve reporting foundations, metric definitions, funnel or journey visibility, and decision-making workflows.
Analyze the quality and long-term value of different customer groups to optimize for durable growth.
Create reusable frameworks and decision tools to help teams evaluate trade-offs across growth, efficiency, customer outcomes, and operational complexity.
Support experimentation and measurement strategies for strategic initiatives, including A/B tests, pilots, campaigns, lifecycle programs, product initiatives, operational changes, and customer-facing programs.
Apply advanced analytics techniques such as impact evaluation, scenario analysis, simulation modelling, forecasting, segmentation, and predictive analytics.
Partner with Data Science on complex modeling opportunities.
Bring strong judgment to ambiguous measurement problems and provide directional decision support.
Help stakeholders understand analytical confidence, limitations, trade-offs, and recommended next actions.
Build trusted relationships with senior leaders and stakeholders by understanding their goals, shaping analytical roadmaps, and proactively identifying opportunities.
Communicate insights through clear, compelling, executive-ready narratives that connect analysis to decisions and business outcomes.
Present findings, recommendations, dashboards, and decision frameworks in business reviews, leadership forums, and cross-functional meetings.
Make trade-offs visible when stakeholder demand exceeds capacity, helping teams prioritize high-impact work.
Collaborate across Analytics & Insights to ensure Customer Analytics work connects with other analytics teams, BI & Analytics Engineering, and Data Science.
Identify recurring analytics work that can be automated, standardized, documented, or moved into self-serve.
Partner with BI & Analytics Engineering to improve the data foundation, semantic layer, reporting infrastructure, and self-serve capabilities.
Use AI-assisted analytics workflows responsibly to accelerate exploration, coding, documentation, QA, and storytelling.
Promote high-quality analytics practices, including clear metric definitions, reproducible workflows, thoughtful QA, documentation, peer review, and data quality stewardship.
Build scalable assets, dashboards, models, and analytical frameworks.
Ensure insights are timely, trusted, actionable, and connected to meaningful business outcomes.
Lead one or more important Customer Analytics domains through influence, ownership, and strong judgment.
Provide mentorship, peer review, and analytical guidance to analysts.
Raise the bar for analytical quality, stakeholder communication, and business impact.
Contribute to team-wide best practices around prioritization, documentation, QA, AI-enabled workflows, self-serve, and strategic storytelling.
Operate as a senior individual contributor who can independently own ambiguous, high-impact work while helping others grow.