Head of Applied Science, AI Delivery

Axis Communications

. Define and communicate the strategy, architecture and investment roadmap for reusable data science and AI capabilities .

Posted 11/10/2026full-timeNew York City • New York • United StatesLead💰 $280,000 - $325,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in defining and delivering AI and data science platform strategies, with a strong focus on lifecycle management, governance, and responsible AI practices. Proven ability to lead multidisciplinary teams and manage complex projects within regulated industries.

Highest-signal resume keywords

Data ScienceMachine LearningGenerative AIAzure AI ServicesMLOps

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

AI Service DevelopmentModel EvaluationDeployment PipelinesIntegration PatternsAnalytics Lifecycle ManagementStatistical AnalysisPython ProgrammingMonitoring ApproachesAgent ArchitecturesEvaluation Techniques

Soft Skills

Excellent CommunicationResource PlanningStakeholder EngagementTeam LeadershipPrioritization

Tools & Technologies

Azure DatabricksAPIsExperimentation EnvironmentsVersion ControlObservability Tools

Industry Keywords

Regulatory ComplianceRisk ManagementInsurance IndustryGovernanceResponsible AI

Tech Stack

Tools & technologies

AzurePython

About the role

Key responsibilities & impact

Define and communicate the strategy, architecture and investment roadmap for reusable data science and AI capabilities
Lead the design and development of shared AI services, analytical models, accelerators, agent frameworks, evaluation frameworks, experimentation environments and common solution components
Partner with Insights & Analytics and value-stream teams to translate recurring needs into platform priorities
Lead experimentation in analytics, machine learning, generative AI and agentic capabilities, converting successful experiments into supported enterprise components
Manage analytics and AI capabilities as products through their full lifecycle
Establish standards for development, testing, deployment, integration, monitoring, observability, version control, documentation and reuse
Embed responsible AI, model risk management, security, privacy and regulatory requirements into platform capabilities
Establish evaluation and monitoring frameworks for model and agent quality, performance, reliability, safety and value
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
Prioritise the platform backlog and manage resources, budgets, suppliers and delivery commitments
Promote adoption and reuse of shared capabilities and identify opportunities to consolidate duplicated solutions
Attend the local office in line with AXIS expectations, engage in company activities and travel internationally occasionally

Requirements

What you’ll need

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
Understanding of the data, analytical, regulatory and risk context of insurance or another highly regulated industry
Strong understanding of enterprise AI engineering, including model and AI service development, evaluation, deployment, monitoring and operation at scale
Experience with modern software engineering and MLOps or LLMOps practices
Strong knowledge of Azure AI services and Azure Databricks
Practical understanding of enterprise data architectures, APIs, deployment pipelines and integration patterns
Experience embedding governance, security, privacy, controls and responsible AI practices
Ability to balance innovation, speed, enterprise value, cost, risk and technical sustainability
Demonstrated success working across analytics, engineering, architecture, technology, governance and senior stakeholders
Proven ability to build, lead and develop multidisciplinary technical teams
Excellent communication skills for explaining complex technical concepts, choices and risks
Ability to manage multiple priorities, resource planning, cost oversight and delivery transparency
Advanced technical education or equivalent professional experience in data science, computer science, statistics, engineering or a related field (preferred)
Track record of building reusable analytics or AI platforms and taking generative AI or agentic capabilities from experimentation into governed enterprise use (preferred)
Strong working knowledge of Python, Databricks, foundational models, evaluation techniques, retrieval patterns, agent architectures, deployment pipelines and monitoring approaches (preferred)
Attend your local office in line with AXIS expectations
Willingness to travel internationally on occasion

Benefits

Comp & perks

Medical plans for you and your family
Health and wellness programs
Retirement plans
Tuition reimbursement
Paid annual leave
Paid vacation
Competitive target incentive compensation based on overall corporate and individual performance