SVP - Data Engineering, Data Science & Analytics at Axs · LumiJobs
senior
$450k–$500k
H1B: no
Axs Senior Vice President of Data Engineering, Data Science & Analytics at AXS leading global data strategy and platforms. Own data infrastructure, analytics, and AI models powering personalization, pricing, and fraud prevention.
Responsibilities
The Senior Vice President of Data Engineering, Data Science & Analytics will lead AXS' global data organization, owning the strategy, platforms, models, and insights that power personalization, pricing, fraud prevention, forecasting, and decision-making across the business. This spans our data lake and warehouse, streaming and batch pipelines, machine learning and AI models, business intelligence and reporting, and the governance practices that make trusted data available to Product, Marketing, Finance, Operations, and executive leadership.
This leader will define a unified vision and roadmap that connects data infrastructure, advanced analytics, and data science, ensuring the organization scales with transaction volume while remaining secure, compliant, and cost-effective — and that every investment translates into measurable business impact.
Success in this role requires close partnership with the CTO, SVP of Software Engineering, Product, and business leaders, as well as the ability to inspire, grow, and scale a world-class, multidisciplinary team. As a senior executive and visible leader, the SVP will champion a data-driven culture, foster innovation and scientific rigor, and ensure data and AI remain a competitive advantage for AXS.
Partner with the CTO and executive leadership to define and advance AXS' enterprise data, analytics, and AI strategy.
Own an integrated roadmap across data platforms, data science, and analytics, balancing long-term vision with near-term priorities in personalization, pricing, fraud, and reporting.
Oversee the design, build, and operation of scalable batch and real-time data pipelines and the evolution of our data lake/warehouse architecture for performance, cost efficiency, and reliability.
Lead the data science function in developing and deploying machine learning and AI models for personalization, dynamic pricing, demand forecasting, fraud detection, and customer lifetime value, with mature MLOps practices.
Build and scale the analytics function, delivering self-service BI, executive dashboards, KPI frameworks, and experimentation (A/B testing) capabilities that drive decisions across the company.
Establish and enforce data governance, quality, lineage, privacy, and responsible AI standards, ensuring compliance with relevant data privacy and payment regulations.
Build and empower a high-performing global organization of data engineers, data scientists, and analysts, fostering a culture of innovation, collaboration, and continuous learning.
Collaborate with Product, Ticketing Strategy, Marketing, Finance, AEG, and other partners to identify high-value opportunities and embed data and models into products and operations.
Drive operational excellence through best practices, disciplined execution, and process improvements that increase reliability, reduce data downtime, and shorten time-to-insight.
