Data science manager Jobs in Halifax, NS

This is your opportunity to join AXIS Capital – a trusted global provider of specialty lines insurance and reinsurance. We stand apart for our outstanding client service, intelligent risk taking and superior risk adjusted returns for our shareholders. We also proudly maintain an entrepreneurial, disciplined and ethical corporate culture. As a member of AXIS, you join a team that is among the best in the industry.

At AXIS, we believe that we are only as strong as our people. We strive to create an inclusive and welcoming culture where employees of all backgrounds and from all walks of life feel comfortable and empowered to be themselves. This means that we bring our whole selves to work.

All qualified applicants will receive consideration for employment without regard to any protected characteristic, including age, color, disability, ethnicity, gender identity, marital status, national origin, pregnancy, race, religion, sex, sexual orientation, veteran status, or any basis prohibited by the laws that govern its operations.

How does this role contribute to our collective success?
Data, analytics and AI are critical to AXIS’s ability to make confident decisions, identify opportunities, improve efficiency and create sustainable competitive advantage. The Head of Applied Science & AI Delivery leads the development, management and evolution of AXIS’s portfolio of reusable applied science and AI capabilities.
Rather than owning delivery for individual business initiatives, this role provides the shared services, models, frameworks, engineering patterns and specialist expertise that enable and support delivery of high-quality business solutions rapidly, safely and consistently. The role is accountable for creating scalable, governed capabilities that can be adopted across multiple value streams, reducing duplication and accelerating value from analytics and AI.

What will you do in this role?

• Set the platform vision and roadmap: Define and communicate the strategy, architecture and investment roadmap for reusable data science and AI capabilities, aligned with AXIS’s business priorities, enterprise technology strategy and Data & Analytics operating model.
• Build reusable capabilities: Lead the design and development of shared AI services, analytical models, accelerators, agent frameworks, evaluation frameworks, experimentation environments and common solution components that can be adopted across multiple business-aligned solutions.
• Enable business-aligned delivery: Partner closely with the Head of Insights & Analytics and value-stream teams to understand recurring needs, translate them into platform priorities and ensure capabilities are practical, discoverable, well documented and easy to adopt.
• Lead experimentation and innovation: Create a controlled environment for exploration of emerging analytics, machine learning, generative AI and agentic capabilities. Convert successful experimentation into supported, repeatable enterprise components where there is clear value.
• Establish product and platform management: Manage analytics and AI capabilities as products through their full lifecycle, with clear ownership, roadmaps, service expectations, adoption measures, technical health indicators and plans for continuous improvement or retirement.
• Lead technical standards and engineering excellence: Establish consistent patterns for development, testing, deployment, integration, monitoring, observability, version control, documentation and reuse. Ensure capabilities are robust, maintainable and designed for enterprise scale.
• Drive responsible and governed AI: 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 by design.
• Own evaluation and monitoring frameworks: Ensure reusable approaches are in place to evaluate model and agent quality, performance, reliability, safety and value, and to monitor deployed capabilities throughout their lifecycle.
• Advance the technology ecosystem: Work closely 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.
• Build and lead a high-performing specialist team: Lead and develop a multidisciplinary team of data scientists, applied scientists and AI engineers. Set a high bar for technical and academic rigour while fostering collaboration, inclusion, curiosity and pragmatic delivery.
• Manage demand, capacity and investment: Prioritise the platform backlog based on enterprise value, reuse potential, risk reduction and strategic fit. Manage resources, budgets, suppliers and delivery commitments transparently.
• Champion adoption and reuse: Promote awareness and effective use of shared capabilities, provide technical enablement to delivery teams and actively identify opportunities to consolidate duplicated solutions or patterns

How this role works with Insights & Analytics

The Insights & Analytics teams are aligned to business value streams and are accountable for identifying opportunities, defining priorities, designing solutions for each business need, delivering business-aligned solutions and driving adoption and measurable value. The Analytics & AI Platforms team is accountable for the reusable technical capabilities, frameworks, standards and specialist expertise that enable those teams to deliver at greater speed, quality and scale.

Both teams share accountability for successful end-to-end outcomes. The platform team does not become a central delivery queue for every use case, and business-aligned teams should reuse enterprise capabilities wherever practical rather than creating parallel solutions.

Success Measures / Key Outcomes

• Reuse and adoption: Shared analytics and AI capabilities are adopted across multiple value streams, with evidence that reuse is reducing duplicated effort and accelerating delivery.
• Speed and scalability: Business-aligned teams can move from opportunity to production more quickly because common services, patterns and deployment pathways are available and reliable.
• Quality and reliability: Platform capabilities meet agreed standards for performance, availability, maintainability, documentation, monitoring and support.
• Responsible delivery: Analytics and AI capabilities operate within AXIS governance, security, privacy, model risk and responsible AI requirements, with effective controls embedded by design.
• Business enablement: Capability investments are demonstrably connected to priority business outcomes and enable solutions that are adopted and deliver measurable value.
• Technology modernisation: The portfolio evolves in a controlled way to take advantage of relevant advances in data science, machine learning, generative AI and agentic technology.
• Team performance: A highly engaged, inclusive and technically strong team with clear accountabilities, effective development pathways and a culture of collaboration and continuous improvement.
• Cost and portfolio discipline: Investment is focused on capabilities with enterprise value and reuse potential, with transparent management of cost, technical debt, suppliers and lifecycle decisions.
  
  

About You
We encourage you to bring your own experience and expertise to the table. While there are qualifications and experiences we need you to have, we are open to discussing how your individual knowledge may help you fulfil this role and contribute to our goals.

What you need to have

• Platform and product leadership: 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.
• Technical leadership: Deep experience in data science, machine learning and AI, including generative AI, with the credibility to set technical direction, challenge design decisions and lead senior specialists.
• Insurance or financial services experience: Understanding of the data, analytical, regulatory and risk context of insurance or another highly regulated industry.
• Enterprise AI engineering: 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.
• Cloud and data platforms: Strong knowledge of Azure AI services and Azure Databricks, together with practical understanding of enterprise data architectures, APIs, deployment pipelines and integration patterns.
• Responsible AI and risk: Experience embedding governance, security, privacy, controls and responsible AI practices into the design and operation of analytics and AI capabilities.
• Strategic and commercial judgement: Ability to balance innovation, speed, enterprise value, cost, risk and technical sustainability when making platform investment and prioritisation decisions.
• Collaborative operating style: Demonstrated success working across business-aligned analytics teams, engineering, architecture, technology, governance and senior stakeholders to deliver shared outcomes without relying solely on direct authority.
• People leadership: Proven ability to build, lead and develop multidisciplinary technical teams and create a high-performance, inclusive and learning-oriented culture.
• Communication and influence: Excellent communication skills, including the ability to explain complex technical concepts, choices and risks clearly to executives, business leaders and delivery teams.
• Delivery leadership: Ability to operate in a fast-paced environment, manage multiple priorities and provide clear direction, resource planning, cost oversight and delivery transparency.
  
  

What we prefer you to have

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

Role Factors

• Attend your local office in line with AXIS expectations to build relationships with colleagues and the wider business.
• Engage in company activities to grow your network and build a strong team culture.
• Travel internationally on occasion, as required by the role.
  
  

What we offer
You will be eligible for a comprehensive and competitive benefits package which includes medical plans for you and your family, health and wellness programs, retirement plans, tuition reimbursement, paid annual leave, and much more.

For this position, we currently expect to offer a base salary in the range of $280,000.00 to $325,000.00. Your salary offer will be based on an assessment of a variety of factors including your specific experience and work location.

In addition, you will be offered competitive target incentive compensation, with awards based on overall corporate and individual performance. On top of this, you will be eligible for a comprehensive and competitive benefits package which includes medical plans for you and your family, health and wellness programs, retirement plans, tuition reimbursement, paid vacation, and much more.

Where this role is based in the United States of America, this role is [Exempt/Non-Exempt] for FLSA purposes.

This posting is for an existing vacancy.