AVP- Data Scientist

1. Advanced Analytics Product DevelopmentDesign and build machine learning models aligned to core personal banking prioritiesIdentify commercially viable use cases in collaboration with product marketing and business stakeholdersDrive the development of analytics products that are scalable measurable and operationally robust2. Model Development and Lifecycle ManagementBuild test and deploy machine learning models into production environmentsEnsure models are aligned with Model Risk Management standards and delivery governanceMonitor model performance and lead retraining or recalibration processes as needed3. MLOps and OperationalisationImplement production-ready model pipelines using CI/CD tooling and automated monitoringEnsure continuity of performance and data integrity throughout the lifecycle4. Feature Engineering and Data ExplorationLead the extraction and transformation of raw data into high-quality featuresConduct deep EDA to identify trends correlations and value-driving insightsUnderstand and navigate complex banking datasets including transactional behavioural and product data5. Business Engagement and CommunicationPresent modelling outcomes and insights to senior non-technical stakeholders with clarity and precisionTranslate business opportunities into concrete data science initiativesProvide clear recommendations and options based on data-driven insights6. Innovation and Delivery FocusDevelop and prototype new modelling techniques and innovative data products with a commercial lensPrioritise delivery and measurable value over theoretical perfectionContribute to the development of reusable frameworks and accelerators to optimise delivery methodologies for data science teamsQualifications :Required Experience and Skills:5 years of applied data science experience in a banking environmentProven track record of deploying production-grade models with ongoing performance managementStrong hands-on skills in SQL Python and ML libraries (e.g. scikit-learn XGBoost)Demonstrated experience in feature engineering and large-scale data handlingFamiliarity with MLOps pipelines and tooling for monitoring and automationStrong commercial acumen and ability to scope and deliver high-impact use casesExcellent presentation communication and stakeholder engagement skillsDeep understanding of model governance standards and regulatory expectationsPreferred Qualifications:Experience with platforms such as Dataiku and Databricks is a significant bonusStrong Retail banking domain knowledgeProven experience with statistical modelling masteryBachelors or Masters degree in a quantitative field (e.g. Computer Science Statistics Engineering)Experience working in cloud environments (Azure AWS or GCP)Remote Work :NoEmployment Type :Full-time#J-18808-Ljbffr