Manager, Advanced Analytics

About the role

The Data Analytics team at Prudential is looking for a highly technical, hands-on Manager, Advanced Analytics to drive the development and deployment of machine learning and AI solutions across the business. The role designs, builds and operationalizes advanced analytical models supporting customer growth, retention, distribution effectiveness, marketing optimization and business transformation. It works closely with the Associate Director, Customer Insights & Advanced Analytics, leading the technical delivery of predictive models, AI applications and modern data science capabilities while helping establish best practices in machine learning engineering and MLOps.

Key responsibilities

• Design, develop, validate and deploy predictive and machine learning models for customer acquisition, retention, repurchase, cross-sell, distribution productivity and marketing effectiveness.
• Build solutions such as propensity models, churn prediction, customer lifetime value models, recommendation engines, forecasting models and optimization algorithms.
• Apply machine learning to improve decisions across customer, agency, marketing and operational domains, and turn business challenges into scalable AI-driven solutions.
• Explore emerging AI technologies (Generative AI, LLMs, RAG, AI agents) and prototype new AI use cases that improve customer experience and operational efficiency.
• Manage the end-to-end ML lifecycle: development, testing, deployment, monitoring and retraining, with feature engineering, hyperparameter tuning and model explainability.
• Implement MLOps best practices and build production-ready models with technology and data engineering teams.

Required profile

• Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Actuarial Science or a related quantitative discipline; Master's degree or above preferred.
• Minimum 8-10 years of experience in data science, machine learning, advanced analytics or related quantitative fields.
• Proven experience delivering machine learning solutions with measurable business impact.
• Experience in insurance, financial services, telecommunications, e-commerce or similar data-intensive industries is preferred.

Required skills

• Machine learning, predictive modeling, statistical analysis and optimization.
• Hands-on Python with libraries such as Scikit-learn, XGBoost, LightGBM, TensorFlow or PyTorch.
• MLOps: deployment, monitoring, version control, CI/CD and production model management.
• Cloud platforms such as Azure, AWS or GCP.
• Familiarity with Generative AI, LLMs, vector databases, RAG and agentic workflows is highly desirable.
• Strong SQL and data manipulation on large structured and unstructured datasets.