Nature & Scope Your Role at Roots
As a member of the senior leadership team, the Sr. Director, Analytics & Insights is accountable for building and leading a best-in-class analytics function that drives commercial strategy, unlocks growth, and enables data-driven decision-making across all areas of the Roots business. Reporting to the Chief Commercial Officer, this leader plays a pivotal role in shaping how Roots identifies and pursues its biggest opportunities - across channels, categories, customers, and markets. This role is responsible for establishing the organizations analytics operating model - including a centralized data and engineering capability and embedded analyst partnerships within Ecommerce, Retail, and Merchandising - and for ensuring that the right insights reach the right people at the right time. As a trusted strategic partner to the CCO and the broader commercial leadership team, the Sr. Director translates data into a competitive advantage: surfacing growth opportunities, informing investment decisions, and bringing an evidence-based perspective to every major commercial initiative. This leader brings both technical credibility and deep commercial acumen, and is as comfortable shaping channel strategy as they are working through a data model with their team.
Key Responsibilities How Youll Make an Impact
Commercial Strategy & Growth
- Act as a strategic growth partner to the CCO and commercial leadership team - proactively identifying opportunities to accelerate revenue, expand market share, improve margin, and deepen customer relationships across all channels.
- Lead the development of commercially-driven analytics that go beyond performance reporting - surfacing white space, sizing opportunities, and informing investment decisions across Ecommerce, Retail, Merchandising, and Marketing.
- Bring a forward-looking perspective to the business: translate trends in customer behaviour, channel performance, and competitive positioning into clear strategic recommendations for the senior leadership team.
- Influence the annual planning process and long-range strategic plan with a data-driven point of view on where Roots should grow, invest, and optimize.
- Champion a test-and-learn culture across commercial functions - developing experimentation frameworks, supporting A/B testing, and building the organizations ability to make faster, evidence-based decisions.
- Stay at the forefront of emerging analytics technologies and AI capabilities - evaluating where tools such as generative AI, predictive modelling, and machine learning can be applied to accelerate insight generation, improve forecasting accuracy, and create competitive advantage for Roots.
Cross-Functional Commercial Partnership
- Serve as the primary analytics partner to Ecommerce, Retail, Merchandising, and Marketing - embedding analytical thinking into their strategies, roadmaps, and operating rhythms.
- Partner with Marketing and CRM to unlock customer analytics capabilities that drive loyalty, retention, and lifetime value growth - including segmentation, win-back, personalization, and loyalty program optimization.
- Collaborate with Merchandising and Planning on assortment strategy, pricing architecture, and inventory investment decisions; bring an analytical lens to buying and open-to-buy processes.
- Support Ecommerce in building a performance marketing and digital analytics capability that connects media spend to revenue outcomes, and identifies the highest-ROI levers for growth.
- Partner with Retail to identify operational and commercial opportunities at the store level, including conversion improvement, traffic optimization, and comp store growth strategies.
Team Leadership & Development
- Lead, coach, and develop a team of analytics professionals across central and embedded functions, fostering a culture of intellectual curiosity, commercial thinking, accountability, and continuous improvement.
- Establish clear roles, responsibilities, and performance expectations for both central team members and business-embedded analysts; ensure alignment and collaboration across the full team.
- Champion the professional growth of each team member through regular feedback, development planning, and exposure to enterprise-wide commercial priorities.
- Build for the future - identify capability gaps, develop succession plans, and invest in the skills needed to grow the analytics function as the business scales.
Analytics Strategy & Operating Model
- Define and execute the analytics strategy for Roots, including the teams operating model, prioritization framework, and approach to self-serve analytics across the business.
- Own the organizations reporting cadence - from daily revenue pulses to quarterly business reviews - ensuring that the business operates from a single, trusted source of truth.
- Establish and govern standards for data definitions, metric frameworks, and analytical methodology across all functions to drive consistency and confidence in reported numbers.
- Build and maintain the analytics roadmap, balancing short-term commercial priorities with longer-term infrastructure investments and capability development.
- Champion a structured approach to ad-hoc analytics requests - including intake, prioritization, SLA management, and the identification of recurring questions that should become standing reports or dashboards.
Data Infrastructure & BI Platforms
- Oversee the data engineering and BI function, ensuring that data pipelines, Snowflake data models, and dashboard infrastructure are reliable, scalable, and accessible to business users.
- Partner with Technology and Finance to evaluate and evolve the analytics tech stack, including BI tooling, data pipeline infrastructure, and data governance platforms.
- Champion data quality and integrity across all reporting; ensure issues are identified proactively and resolved with urgency.
- Drive the adoption of self-serve analytics tools and capabilities that reduce dependency on the central analytics team for routine reporting needs.
- Identify and evaluate opportunities to integrate AI and machine learning into the analytics stack - including demand forecasting, customer propensity modelling, personalization, and automated anomaly detection - and build a roadmap for adoption that is practical, scalable, and aligned to business priorities.
Qualifications & Experience The Skills You Bring
- 15+ years of progressive analytics or data leadership experience, with a minimum of 5 years leading analytics teams in a retail, ecommerce, or consumer brand environment.
- Demonstrated track record of building and scaling analytics functions that have driven measurable commercial outcomes - revenue growth, margin improvement, customer retention, or channel expansion.
- Deep expertise across the retail commercial analytics landscape - including ecommerce performance, customer and loyalty analytics, merchandising and inventory analytics, marketing effectiveness, and competitive intelligence.
- Proven ability to act as a strategic growth partner to commercial leaders - not just reporting performance, but proactively identifying opportunities, shaping strategy, and influencing investment decisions through data.
- Strong technical foundation with hands-on experience in SQL, data modelling, and BI platforms (experience with Snowflake is a strong asset); able to engage credibly with data engineers and evaluate technical approaches.
- Exceptional executive communication and storytelling skills - able to distill complex analytical findings into compelling commercial narratives, present with confidence to the C-suite and Board, and drive alignment through insights.
- Proven ability to operate in an embedded analytics model, building trusted relationships with functional leaders while maintaining centralized standards and a cohesive team culture.
- Experience building and managing structured analytics operating rhythms, including regular reporting cadences, ad-hoc request processes, and roadmap planning.
- Bachelors degree in a quantitative field (Mathematics, Statistics, Computer Science, Economics, or related); Masters degree or MBA is an asset.
- Demonstrated curiosity and working knowledge of AI and machine learning applications in a retail or commercial context - including experience evaluating or deploying predictive models, generative AI tools, or automation capabilities that have driven measurable business outcomes.
