首席分析官（CAO）— 保险与投资
Chief Analytics Officer (CAO) — Insurance & Investments
职位概述
首席分析官（CAO）是负责利用企业数据、高级分析、人工智能和商业智能来改进战略和运营决策的高管领导者。
在保险与投资领域，CAO通常负责承保、理赔、定价、客户洞察、欺诈、风险、精算见解、投资表现、组合分析、财务规划、监管报告和企业绩效等方面的分析工作。
主要职责
制定并执行组织的企业分析战略。
建立企业范围内的分析运营模式、路线图、标准和优先事项。
领导高级分析、预测分析、AI/ML、数据科学和商业智能。
将复杂的数据转化为对CEO、董事会、高管和业务领袖有行动价值的见解。
发展分析能力包括：
承保和风险选择
理赔和损失分析
定价和盈利能力
客户细分和留存
欺诈检测
风险和资本分析
组合和投资表现
资产配置
市场和投资研究
财务预测
监管和管理报告
建立企业分析KPI和衡量框架。
推动预测建模和情景分析以支持战略决策。
与CIO/CDO/CTO合作，推进数据架构、数据平台、治理、集成和技术现代化。
确保分析模型使用可靠、受控、安全且适当管理的数据。
开发自助式BI和高管仪表盘。
识别业务流程中AI和自动化的机遇。
建立模型治理、验证、监控和负责任的AI实践。
支持精算、风险、财务、投资、合规和高管决策。
领导企业分析转型和数据驱动文化。
组建、管理和培养在分析、数据科学、BI和AI方面表现优异的团队。
管理分析预算、供应商、战略技术合作伙伴和外部数据提供方。
向高级管理层和董事会传达分析结果和业务影响。
确保分析活动符合监管、隐私、风险和金融服务要求。
理想的候选人背景
15年以上在分析、数据科学、BI、统计学、定量分析或相关领域的经验。
7年以上高级分析/数据领导经验。
查看英文原文
Job Summary
The Chief Analytics Officer (CAO) is the executive leader responsible for using enterprise data, advanced analytics, AI, and business intelligence to improve strategic and operational decision-making.
Within Insurance & Investments, the CAO typically oversees analytics across underwriting, claims, pricing, customer intelligence, fraud, risk, actuarial insights, investment performance, portfolio analytics, financial planning, regulatory reporting, and enterprise performance.
Key Responsibilities
Define and execute the organization's enterprise analytics strategy.
Establish an enterprise-wide analytics operating model, roadmap, standards, and priorities.
Lead advanced analytics, predictive analytics, AI/ML, data science, and business intelligence.
Translate complex data into actionable insights for CEO, Board, executives, and business leaders.
Develop analytics capabilities for:
Underwriting and risk selection
Claims and loss analysis
Pricing and profitability
Customer segmentation and retention
Fraud detection
Risk and capital analytics
Portfolio and investment performance
Asset allocation
Market and investment research
Financial forecasting
Regulatory and management reporting
Establish enterprise analytics KPIs and measurement frameworks.
Drive predictive modelling and scenario analysis to support strategic decisions.
Partner with the CIO/CDO/CTO on data architecture, data platforms, governance, integration, and technology modernization.
Ensure analytical models use reliable, governed, secure, and appropriately controlled data.
Develop self-service BI and executive dashboards.
Identify opportunities for AI and automation across business functions.
Establish model governance, validation, monitoring, and responsible AI practices.
Support actuarial, risk, finance, investment, compliance, and executive decision-making.
Lead enterprise analytics transformation and data-driven culture.
Build, manage, and develop high-performing teams across analytics, data science, BI, and AI.
Manage analytics budgets, vendors, strategic technology partners, and external data providers.
Communicate analytical findings and business implications to senior executives and the Board.
Ensure analytics activities align with regulatory, privacy, risk, and financial-services requirements.
Ideal Candidate Profile
15+ years of experience across analytics, data science, BI, statistics, quantitative analysis, or related fields.
7+ years in senior analytics/data leadership.
Previous experience as:
Chief Analytics Officer
Chief Data & Analytics Officer
Chief Data Officer
Head of Data & Analytics
Head of Analytics
Group Head of Analytics
Director of Data & Analytics
Analytics Director
VP Data & Analytics
Strong experience in Insurance, Banking, Investment Management, Asset Management, Wealth Management, or Financial Services.
Strong understanding of:
Advanced analytics
Predictive modelling
AI/ML
Data science
Business intelligence
Data strategy
Data governance
Data architecture
Statistical modelling
Data visualization
Scenario/stress testing
Experience converting analytics into measurable business outcomes.
Strong executive stakeholder management and Board-level communication.
Experience leading large, multidisciplinary analytics/data teams.
Strong commercial and strategic understanding of financial-services businesses.
Bachelor's degree required; Master's/PhD in Data Science, Statistics, Mathematics, Computer Science, Economics, Finance, Actuarial Science, or a related field is advantageous.
Originally posted on Himalayas