Head of Applied Science and AI Delivery

AXIS (AXIS Capital) • Halifax, NS

1d • $280,000 - $325,000

About The Position

The Head of Applied Science and AI Delivery leads the development, management and evolution of AXIS Capital’s portfolio of reusable applied science and AI capabilities. The role provides shared services, models, frameworks, engineering patterns and specialist expertise to help business-aligned teams deliver high-quality solutions rapidly, safely and consistently. It is accountable for creating scalable, governed capabilities that can be adopted across multiple value streams, reducing duplication and accelerating value from analytics and AI.

The role defines the vision, architecture and investment roadmap for reusable data science and AI capabilities; builds shared AI services, analytical models, accelerators, agent and evaluation frameworks, experimentation environments and common solution components; and manages these capabilities as products through their full lifecycle. It partners with Insights & Analytics, value-stream teams, Data Platforms & Engineering, Business Technology Solutions and governance, risk, legal, compliance and technology stakeholders.

The position establishes technical standards and engineering practices, embeds responsible AI and risk controls, advances the analytics and AI technology ecosystem across Azure and Databricks, and leads a multidisciplinary team of data scientists, applied scientists and AI engineers. It also prioritises platform demand, manages resources, budgets, suppliers and delivery commitments, and promotes adoption and reuse across the organisation.

The role is expected to attend a local office in line with AXIS expectations, participate in company activities and travel internationally occasionally as required. The anticipated base salary range is $280,000 to $325,000, with competitive target incentive compensation and a comprehensive benefits package.

Requirements

• Significant experience defining and delivering a platform, product or reusable capability strategy in a complex organisation, with accountability for roadmaps, prioritisation, adoption and lifecycle management.
• Deep experience in data science, machine learning and AI, including generative AI, and the credibility to set technical direction, challenge design decisions and lead senior specialists.
• Understanding of data, analytical, regulatory and risk contexts in insurance or another highly regulated industry.
• Strong understanding of how analytical models and AI services are developed, evaluated, deployed, monitored and operated at scale, including modern software engineering and MLOps or LLMOps practices.
• Strong knowledge of Azure AI services and Azure Databricks, and practical understanding of enterprise data architectures, APIs, deployment pipelines and integration patterns.
• Experience embedding governance, security, privacy, controls and responsible AI practices into the design and operation of analytics and AI capabilities.
• Ability to balance innovation, speed, enterprise value, cost, risk and technical sustainability in platform investment and prioritisation decisions.
• Demonstrated success collaborating across business-aligned analytics teams, engineering, architecture, technology, governance and senior stakeholders to deliver shared outcomes without relying solely on direct authority.
• Proven ability to build, lead and develop multidisciplinary technical teams and create a high-performance, inclusive and learning-oriented culture.
• Excellent communication skills, including the ability to explain complex technical concepts, choices and risks clearly to executives, business leaders and delivery teams.
• Ability to work in a fast-paced environment, manage multiple priorities and provide clear direction, resource planning, cost oversight and delivery transparency.

Nice To Haves

• Advanced technical education or equivalent professional experience in data science, computer science, statistics, engineering or a related field.
• A track record of building reusable analytics or AI platforms and taking generative AI or agentic capabilities from experimentation into governed enterprise use.
• Strong working knowledge of Python, Databricks, foundational models, evaluation techniques, retrieval patterns, agent architectures, deployment pipelines and monitoring approaches.

Responsibilities

• Define and communicate the strategy, architecture and investment roadmap for reusable data science and AI capabilities, aligned with business priorities, enterprise technology strategy and the Data & Analytics operating model.
• Lead the design and development of shared AI services, analytical models, accelerators, agent frameworks, evaluation frameworks, experimentation environments and reusable solution components.
• Partner with the Head of Insights & Analytics and value-stream teams to understand recurring needs, set platform priorities and make capabilities practical, discoverable, documented and easy to adopt.
• Create a controlled environment to explore emerging analytics, machine learning, generative AI and agentic capabilities, and turn successful experimentation into supported enterprise components where value is clear.
• Manage analytics and AI capabilities as products throughout their lifecycle, including ownership, roadmaps, service expectations, adoption measures, technical health, continuous improvement and retirement planning.
• Establish standards and engineering patterns for development, testing, deployment, integration, monitoring, observability, version control, documentation and reuse.
• Partner with Data & AI Governance, Information Risk Management, Legal, Compliance and Technology teams to embed responsible AI, model risk management, security, privacy and regulatory requirements into platform capabilities.
• Ensure reusable approaches are available to evaluate model and agent quality, performance, reliability, safety and value, and to monitor deployed capabilities throughout their lifecycle.
• Work with Data Platforms & Engineering and Business Technology Solutions to shape and implement the analytics and AI technology stack, integration patterns and deployment pathways across Azure and Databricks.
• Lead and develop a multidisciplinary team of data scientists, applied scientists and AI engineers, fostering technical rigour, collaboration, inclusion, curiosity and pragmatic delivery.
• Prioritise the platform backlog based on enterprise value, reuse potential, risk reduction and strategic fit; manage resources, budgets, suppliers and delivery commitments transparently.
• Promote awareness and use of shared capabilities, provide technical enablement to delivery teams and identify opportunities to consolidate duplicated solutions or patterns.
• Attend the local office in line with AXIS expectations, engage in company activities and travel internationally occasionally as required.

Benefits

• Medical plans for you and your family
• Health and wellness programs
• Retirement plans
• Tuition reimbursement
• Paid annual leave or paid vacation
• Competitive target incentive compensation based on overall corporate and individual performance
• Comprehensive and competitive benefits package

What This Job Offers

• Job Type: Full-time
• Career Level: Director
• Education Level: No Education Listed