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About the Role

The Lead Applied Scientist is the senior scientific authority in the Data Science and AI Delivery team, responsible for the quality of AI produced, increasingly using large language models and agentic approaches. This role leads the entire lifecycle of AI solutions, from problem framing to production monitoring, setting scientific standards to ensure business confidence.

Responsibilities

• Lead the team’s approach to solving business problems through advanced analytics and AI.
• Guide how solutions are designed and built.
• Contribute directly to technical work such as model development, experimentation, code review and solution architecture.
• Lead the design and development of the team’s AI and ML solutions, choosing the right approach for each problem, with large language models and agentic AI increasingly central to the work.
• Bring mathematical and statistical rigor to problem framing, uncertainty handling, and solution evaluation.
• Build and evaluate solutions to the most demanding problems.
• Review the work of others to maintain high standards.
• Act as the point of escalation for difficult technical decisions.
• Hold sign-off on scientific approach and solution quality.
• Frame business problems so that the right approach can be chosen and the result measured against clear success criteria.
• Lead the design of generative and agentic AI solutions, including prompting, retrieval augmented generation, tool use and multi-step agent workflows, and the techniques needed to make them accurate and reliable.
• Select the right approach for each problem, with large language models and agentic AI to the fore, and drawing on deep learning, machine learning and statistical methods where they are the better fit.
• Own the evaluation and accuracy methodology for the team’s models, agents and AI systems, including metrics, test sets and acceptance thresholds, with proper treatment of uncertainty and statistical significance, and monitoring for drift.
• Lead the team’s responsible AI work, including bias and fairness testing, explainability, and validation of model and agent behavior against regulatory expectations.
• Solve the team’s most challenging problems, such as extracting information from unstructured documents, automating expert workflows with agents, optimization, forecasting, and portfolio and claims analytics.
• Own the scientific design decisions on each solution, including choice of approach, model and method selection, and evaluation strategy, while working closely with the engineering lead on architecture and deployment.
• Write production quality code and build solutions directly, particularly on novel or higher risk work.
• Set the experimentation, evaluation, coding and documentation standards for the team, and raise them through code review, pairing and technical mentoring.
• Explain methods, results and trade-offs clearly to business stakeholders, model risk, and governance forums.

Requirements

• Strong, hands-on expertise with large language models and generative AI, including prompt engineering, retrieval augmented generation, fine tuning, and the design and evaluation of agentic systems that use tools and operate over multiple steps.
• Strong programming ability in Python and fluency with modern AI and data science tooling, including frameworks for building with large language models alongside libraries such as pandas, NumPy and scikit-learn.
• Strong mathematical and statistical foundations, including probability, statistics, linear algebra and optimization, and the ability to reason rigorously about uncertainty, error and model behavior.
• A sound grounding in machine learning, including model validation and experimental design, applied where it is the right tool for the problem.
• Demonstrable experience designing evaluation and validation frameworks for AI systems, including methods for measuring the quality of large language model, retrieval augmented generation and agent outputs.
• A strong track record of building AI solutions that solve real business problems and of taking them from prototype to production.
• Practical experience with Databricks, MLflow and Spark based data processing.
• Working knowledge of responsible AI methods, including bias and fairness testing, explainability techniques, and model risk.
• The ability to explain complex technical concepts clearly to non-technical and senior audiences.
• A track record of setting technical standards and developing other scientists and engineers.

Skills

• Large language models
• Generative AI
• Prompt engineering
• Retrieval augmented generation
• Fine tuning
• Agentic systems
• Python
• Data science tooling
• Deep learning
• Machine learning
• Statistical methods
• Probability
• Statistics
• Linear algebra
• Optimization
• Model validation
• Experimental design
• Databricks
• MLflow
• Spark
• Responsible AI
• Bias and fairness testing
• Explainability
• Model risk

Location

• Halifax, Nova Scotia
• New York, NY

Work Type

• Onsite (at least three days per week)

Experience Level

• Expertise: A recognized specialist in applied AI, including large language models and agentic systems, underpinned by strong mathematical and statistical foundations, who sets the scientific approach for the team and advises the wider function.
• Relationship Management: Acts as a trusted technical advisor. Influences senior stakeholders and governance forums, represents the scientific view of the function, and develops other members of the team.
• Complexity and Strategic Impact: Sets the approach for new and ambiguous problems where no established method exists. The work is strategic, the planning horizon spans quarters, and the decisions affect the reliability and trustworthiness of the team’s models, agents and AI systems.
• Autonomy and Authority: Works with a high degree of independence and is the technical authority that others escalate to. Holds sign-off on scientific approach and solution quality.
• Contribution: Sets best practice and standards for the design and evaluation of AI systems, including generative and agentic AI, responsible AI and overall solution quality, shapes how the team works, and drives improvements that raise the quality and confidence of the team’s AI work.

Education Level

• A degree in a mathematical, statistical or computational field, often at postgraduate level

Salary/Compensations

• $110,000 - $130,000 CAD (Halifax, Nova Scotia)
• $175,000 - $200,000 USD (New York, NY)

Benefits

• Competitive target incentive compensation
• Medical plans for you and your family
• Health and wellness programs
• Retirement plans
• Tuition reimbursement
• Paid vacation

About the Company

• AXIS Capital is 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 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.

Equal Opportunity

• 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.