• Define and lead Direct to Consumer (DTC) eCommerce data strategy, aligning digital analytics with growth, customer acquisition, and revenue objectives • Drive profitable growth by linking marketing spend, merchandising decisions, and customer behavior to Lifetime Value (LTV), margin and retention • Establish advanced marketing frameworks, Marketing Mix Modeling (MMM), Multi-Touch Attribution (MTA) incrementality to optimize marketplace performance • Shape customer lifecycle strategy through cohort analysis, segmentation, and personalization across acquisition, conversion and retention • Develop forecasting and predictive models to support demand planning, inventory strategy and revenue growth • Partner with leadership to influence pricing, promotional strategy, and digital investment decisions • Identify and implement new tools and technologies to enhance digital analytics and personalization capabilities • Oversee end-to-end DTC funnel performance, including homepage, category pages, Product Detail Pages (PDPs), cart, and checkout optimization • Manage digital experimentation strategy, including A/B and multivariant testing, to improve conversion rate and Average Order Value (AOV) • Oversee acquisition performance across paid media, Search Engine Optimization (SEO), email, SMS, and affiliate channels • Ensure robust data collection, tagging, and governance across analytics platforms and tools • Develop and maintain executive dashboards with consistent key performance indicators (KPIs) across channels and campaigns • Drive cross-functional alignment across marketing, merchandising, operations, and technology teams to execute data-driven initiatives • Support and maintain a positive safety culture by following all safety policies and procedures and actively contributing to a safe working environment

• Lead the strategy and execution of all email and SMS marketing campaigns. • Build and optimize automated lifecycle flows (welcome series, abandoned cart, post-purchase, winback, etc.) • Develop and maintain a high-level campaign calendar aligned with marketing and business goals • Analyze performance metrics (open rate, CTR, conversion, revenue) and continuously optimize campaigns. • Ensure proper audience segmentation and targeting for personalized customer journeys

• Manage and execute merchandising decisions on our client's website, including promotions, product placement, and pricing strategies. • Optimize revenue, average order value (AOV), and profit margin through data-driven merchandising strategies. • Collaborate with internal teams (Marketing, Finance, Pricing, and Partnerships) to align promotional efforts and revenue goals. • Assist in managing sports partnership programs, working with major league, minor league, and collegiate teams to drive brand engagement and sales. • Support affiliate and partnership initiatives, helping recruit and manage relationships with collegiate and professional athletes. • Track and analyze merchandising performance, using data to make informed recommendations for future optimizations. • Oversee the merchandising of new product launches and ensure seamless execution across digital platforms. • Conduct regular quality assurance (QA) checks on merchandising pages to maintain a high standard of accuracy and usability.

• Serve as the primary analytics leader across eCommerce, Marketing, Finance, Retail, Merchandising, Inventory Planning, and Operations. • Own problem definition for analytics - determining which business questions matter and what decisions insights should inform. • Move analysis beyond “what happened” to “why it happened,” “how it happened,” and “what we should do next.” • Proactively mine data to identify risks, opportunities, inefficiencies, and performance drivers. • Influence senior leaders through clear implications and actionable recommendations. • Own the integrity, governance, and usability of core commerce, customer, and operational datasets across platforms (eCommerce, POS, CDP, email, data warehouse). • Validate insights using business context and commercial logic — not just technical correctness. • Define, document, and continuously evolve company-wide KPI frameworks in partnership with Finance and functional leaders. • Identify and resolve cross-functional metric misalignment to ensure a single, trusted understanding of performance. • Establish standards for data quality, documentation, and analytical transparency across the organization. • Leverage AI as a core component of advanced analytical work, including hypothesis generation, exploratory analysis, pattern detection, insight synthesis, and narrative development. • Design, operationalize, and scale repeatable AI-assisted workflows that support recurring company-level decisions (growth, margin optimization, inventory productivity, forecast alignment). • Establish validation frameworks to pressure-test AI-generated outputs against source data, historical performance, customer behavior, and commercial logic. • Embed AI-enabled insights into planning, forecasting, and executive performance reviews. • Promote responsible, explainable, and outcome-driven AI usage across analytics workflows. • Own web and digital analytics strategy using Heap, including funnel performance, customer journey analysis, and conversion optimization. • Ensure tracking integrity and event schema accuracy, including review of structured data formats (e.g., JSON, data layers) in partnership with Product Engineering. • Translate behavioral data into commercial implications that influence merchandising, marketing, inventory, and operational decisions. • Ensure behavioral insights are aligned with downstream systems and financial realities. • Reconcile demand, margin, inventory, and operational perspectives into cohesive performance narratives. • Support planning, forecasting, and performance alignment across Finance, Retail, Merchandising, and Operations. • Challenge assumptions and reframe business narratives using data-backed insight. • Serve as a connector between commercial strategy and operational execution. • Establish analytical standards that prioritize clarity, trust, commercial relevance, and decision impact. • Reduce reactive ad hoc work by building scalable, decision-focused reporting and automation frameworks. • Design the structure, workflows, and expectations for the analytics function. • Hire, mentor, and develop junior analytics talent once foundational systems are established. • Champion a modern, insight-driven analytics culture across the company.