Job Description:
The Head of Machine Learning will lead the design, development, and deployment of advanced ML solutions to transform insurance broking and underwriting operations. This role requires deep expertise in insurance domain analytics and Databricks-based machine learning pipelines, coupled with strong leadership skills to drive innovation, efficiency, and profitability.
The ideal candidate will oversee the full ML lifecycle — from ideation and modeling to deployment and monitoring — while collaborating closely with underwriting, actuarial, broking, and technology teams.
Responsibilities:
Strategic Leadership
• Define the organization’s ML strategy for insurance broking and underwriting, aligning with business objectives. • Partner with underwriting and broking leadership to identify high-value ML use cases (e.g., risk scoring, pricing optimization, customer segmentation, fraud detection). • Champion data-driven decision-making across the organization. ML
Solution Development
• Design and build predictive and prescriptive models using Databricks
Machine Learning (MLflow, Delta Lake, AutoML, Feature Store).
• Leverage historical policy, claims, market, and broker data to develop models for risk assessment, quote optimization, and cross-sell/up-sell strategies. • Conduct exploratory data analysis (EDA) to identify trends, anomalies, and opportunities. • Deploy models into production environments with robust monitoring and retraining pipelines.
Platform & Data Management
• Architect scalable ML pipelines leveraging Databricks, Spark, and Delta Lake. • Integrate external data sources (market data, credit data, weather data, etc.) into ML workflows. • Ensure compliance with insurance regulations and data governance policies (GDPR, CCPA, Solvency II).
Collaboration & Stakeholder Engagement
• Work closely with brokers, underwriters, and actuaries to embed ML insights into daily workflows. • Collaborate with data engineering teams to ensure clean, high-quality, and accessible data. • Present ML outputs and business impact in clear, non-technical terms for executives.
Team Leadership
• Build and lead a multidisciplinary team of ML engineers, data scientists, and domain experts in a federated operating environment. • Foster continuous learning in ML, AI ethics, insurance analytics, and regulatory compliance. • Implement agile development practices for rapid iteration and delivery.
Innovation & Continuous Improvement
• Evaluate emerging AI/ML techniques, including deep learning, NLP, and graph analytics, for insurance applications. • Drive experimentation with real-time scoring and decision-support tools. • Promote explainable AI (XAI) for transparency in underwriting decisions.
Qualifications:
Education & Experience
• Bachelor’s or Master’s in Data Science, Computer Science, Statistics, or related field (PhD preferred).
• 10+ years in data science/ML, with 5+ years in insurance analytics or underwriting technology leadership.
• Proven experience implementing ML solutions in insurance broking and underwriting contexts.
• Hands-on expertise with Databricks ML, MLflow, Spark, and Delta Lake.
Desired Skills:
• Proficiency in Python, SQL, and relevant ML libraries (scikit-learn, TensorFlow, PyTorch, etc.).
• Strong understanding of feature engineering, model validation, and performance optimization.
• Experience deploying ML models in production at scale. • Knowledge of API integration for embedding ML outputs into broker/underwriter systems. Domain Skills
• Deep understanding of insurance product lines, risk models, and underwriting processes.
• Familiarity with rating engines, actuarial models, and market placement platforms.
• Awareness of insurance regulations, compliance, and ethics in AI use. Leadership & Soft Skills
• Exceptional communication and stakeholder management abilities.
• Ability to bridge business needs and technical solutions. • Strategic mindset with a bias toward measurable business outcomes. Success/Performance Metrics:
• Accuracy and business lift from deployed ML models.
• Reduction in underwriting turnaround time through ML automation.
• ROI from ML initiatives in broking and underwriting.
• Adoption rates of ML-driven tools by business teams.
• Model compliance with regulatory and ethical standards
