Business Engagement Manager

CONA Services•Toronto, ON

1d•Hybrid

About The Position

We are recruiting a Business Engagement Manager to join our Data Science & Commercial Analytics team. This role serves as a key bridge between the Advanced Analytics function and the broader business, translating the work of data scientists—such as demand forecasting, pricing and promotion optimization, geospatial modeling, and customer segmentation—into clear, actionable insights for Sales, Revenue Growth Management, Operations, and Leadership. In this role, you will help ensure that complex analytical work is understood, trusted, and ultimately adopted to drive business impact. You will also play a central role in shaping and communicating our AI roadmap—developing the narrative, building executive-ready materials, and leading enablement efforts to help teams understand and effectively use the tools and capabilities being developed. This is an exciting opportunity to work at the intersection of data, strategy, and business execution. You will gain broad exposure across functions, influence high-impact decisions, and play a critical role in turning advanced analytics into real-world outcomes. Success in this role means not just delivering insights but ensuring they are embedded into how the business operates and makes decisions.

Requirements

Bachelor’s degree in Business, Economics, Computer Science, Data Science, Finance, or a related field.
3+ years of experience in analytics, data science, consulting, or a related field.
Strong ability to translate complex technical concepts into clear, business-focused insights for senior stakeholders.
Working knowledge of modern analytics, machine learning, and AI concepts (e.g., regression, tree-based models, neural networks, LLMs, and applied concepts such as elasticity and optimization).
Proven ability to facilitate discussions and manage cross-functional projects, working effectively with both technical and non-technical teams.
Strong critical thinking skills with a practical, solution-oriented mindset, prioritizing actionable outcomes over perfection.
Exceptional interpersonal and relationship-building skills, with the ability to influence stakeholders across different levels of the organization.
Excellent communication and presentation skills, with the ability to structure clear, compelling, data-driven narratives.

Nice To Haves

Master’s degree (e.g., MBA or related field) is considered an asset.
Experience developing or delivering training, enablement programs, or supporting change management initiatives is an asset.
Experience in a CPG, retail, or other customer-facing environment is considered an asset.

Responsibilities

Act as the primary interface between Advanced Analytics and the functions we serve: sales, revenue management, commercial strategy, operations, and the leadership team.
Translate complex analytical and AI work into clear, compelling stories for non-technical audiences — written decks, live presentations, and working sessions.
Manage alignment across stakeholders from ideation through development and execution, making sure the right people are engaged at the right moments and business needs are reflected in what we build.
Partner with the Director on shaping and communicating the Advanced Analytics and AI roadmap — the strategy, the narrative, and the sequencing of initiatives.
Build executive-level presentations on AI initiatives, POCs, and roadmap priorities; own the deck craft end-to-end.
Design and deliver internal training sessions to build AI and analytics literacy across the business, including on the tools we build in-house and the platforms we adopt.
Develop enablement materials that help leaders and frontline teams understand what our AI capabilities can and can’t do, and how to use them well in day-to-day decisions.
Support change management as new analytics and AI tools land in the business — adoption plans, office hours, feedback loops back to the team.
Run the project rituals that keep multi-stakeholder analytics work on track: steering committees, working sessions, documentation, timelines, and measurement.
Own post-implementation audits — did the model hold up, did the promo perform the way we predicted, where are the gaps, what do we do about them.
Support ad-hoc deep dives and productivity projects using POS, shipment, demographic, brand attribute, customer, and financial data.
Contribute to trade spend optimization, promotion testing, innovation cannibalization analysis, store clustering, and channel pilot projects. You won’t have to build the models, but you need to understand them well enough to represent them credibly.
Identify and lead process improvement projects across the team and the functions we work with.
Capture what works and what doesn’t, and feed it back into how the team operates.