Head Machine Learning

• 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.

• 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.

• 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).

• 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.

• 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.

• 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.
• 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.
• 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.
• Exceptional communication and stakeholder management abilities.
• Ability to bridge business needs and technical solutions.
• Strategic mindset with a bias toward measurable business outcomes.

Alliance of Professionals & Consultants