• Apply statistical, machine learning, and econometric methods to large datasets to evaluate channel performance and isolate the causal impact of marketing and sales activity on a multi-touch B2B sales cycle • Build attribution models and investment frameworks that inform Spellbook's channel spend, helping marketing and finance understand which messages resonate with which segments at each stage of the funnel • Partner with Marketing Ops, Sales, and Engineering to unify data across first- and third-party sources, so every growth decision is made with full context • Drive experimental design across acquisition, activation, and retention surfaces, enabling fast, low-cost iteration on new channels and messaging • Shape the data culture at Spellbook by improving the tools, processes, and systems the broader team uses to make decisions at scale

• Own and manage Gravie's core marketing technology stack • Evaluate the existing marketing technology stack quickly • Partner with marketing leadership to develop business cases • Design and build automation architecture • Maintain data integrity across the marketing stack • Build and maintain measurement frameworks • Act as an internal technical partner to the full marketing team • Own integrations between marketing systems and adjacent platforms • Evaluate and recommend additions or changes to the marketing technology stack • Ensure compliance with data privacy requirements • Establish reporting infrastructure and dashboards

• Oversee the end-to-end product operations cycle, ensuring timely and efficient delivery of features, improvements, and customer-facing solutions. • Collaborate with product and engineering teams to align operational processes with product goals, ensuring that customer needs are met with scalable solutions. • Establish, track, and optimize key performance metrics that ensure product and operational success, identifying areas for continuous improvement. • Manage internal and external communication around product updates, resolving any operational roadblocks that arise. • Use data-driven insights to refine operational strategies, ensuring seamless execution and support for product teams.

• Design data collection workflows optimized for particular model needs • Implement quality controls to reduce biases and ensure model safety • Run experiments to make leading models more insightful, compelling, safe, and human aligned