Design, develop, validate, and deploy predictive and machine learning models to support customer acquisition, retention, repurchase, cross-sell, distribution productivity, and marketing effectiveness.
Develop advanced analytical solutions including propensity models, churn prediction, customer lifetime value models, recommendation engines, forecasting models, and optimization algorithms.
Apply modern machine learning techniques to identify business opportunities and improve decision making across customer, agency, marketing, and operational domains.
Translate business challenges into scalable analytical and AI-driven solutions.
AI & Machine Learning Innovation
Explore and implement emerging AI technologies, including Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI agents where appropriate.
Evaluate and prototype new AI use cases that enhance customer experience, operational efficiency, and business productivity.
Drive innovation by identifying opportunities to leverage AI and advanced analytics across the organization.
Model Engineering & MLOps
Establish and manage end-to-end machine learning lifecycle processes, including model development, testing, deployment, monitoring, and retraining.
Optimize model performance through feature engineering, hyperparameter tuning, model selection, and experimentation.
Develop scalable and production-ready machine learning solutions in collaboration with technology and data engineering teams.
Implement MLOps best practices to ensure model reliability, governance, scalability, and operational efficiency.
Job Requirements
Education
Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Actuarial Science, or a related quantitative discipline.
Master's degree or above is preferred.
Experience
Minimum 8-10 years of experience in data science, machine learning, advanced analytics, or related quantitative fields.
Proven experience in developing and deploying machine learning solutions that deliver measurable business impact.
Experience within insurance, financial services, telecommunications, e-commerce, or other data-intensive industries is preferred.
Technical Expertise
Strong expertise in machine learning, predictive modeling, statistical analysis, and optimization techniques.
Hands-on experience with Python and machine learning libraries such as Scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch, or equivalent.
Experience in feature engineering, model evaluation, hyperparameter optimization, and model explainability.
Strong understanding of MLOps, including model deployment, monitoring, version control, CI/CD, and production model management.
Experience working with cloud-based analytics and AI platforms such as Azure, AWS, or GCP.
Familiarity with Generative AI, LLMs, vector databases, RAG architectures, agentic workflows, and modern AI technologies is highly desirable.
Strong SQL and data manipulation capabilities with experience handling large-scale structured and unstructured datasets.
Prudential is an equal opportunity employer. We provide equality of opportunity of benefits for all who apply and who perform work for our organisation irrespective of sex, race, age, ethnic origin, educational, social and cultural background, marital status, pregnancy and maternity, religion or belief, disability or part-time / fixed-term work, or any other status protected by applicable law. We encourage the same standards from our recruitment and third-party suppliers taking into account the context of grade, job and location. We also allow for reasonable adjustments to support people with individual physical or mental health requirements.
