AVP- Data Scientist

1. Advanced Analytics Product Development

• Design and build machine learning models aligned to core personal banking priorities
• Identify commercially viable use cases in collaboration with product marketing and business stakeholders
• Drive the development of analytics products that are scalable measurable and operationally robust

2. Model Development and Lifecycle Management

• Build test and deploy machine learning models into production environments
• Ensure models are aligned with Model Risk Management standards and delivery governance
• Monitor model performance and lead retraining or recalibration processes as needed

3. MLOps and Operationalisation

• Implement production-ready model pipelines using CI/CD tooling and automated monitoring
• Ensure continuity of performance and data integrity throughout the lifecycle

4. Feature Engineering and Data Exploration

• Lead the extraction and transformation of raw data into high-quality features
• Conduct deep EDA to identify trends correlations and value-driving insights
• Understand and navigate complex banking datasets including transactional behavioural and product data

5. Business Engagement and Communication

• Present modelling outcomes and insights to senior non-technical stakeholders with clarity and precision
• Translate business opportunities into concrete data science initiatives
• Provide clear recommendations and options based on data-driven insights

6. Innovation and Delivery Focus

• Develop and prototype new modelling techniques and innovative data products with a commercial lens
• Prioritise delivery and measurable value over theoretical perfection

Contribute to the development of reusable frameworks and accelerators to optimise delivery methodologies for data science teams

Qualifications :

Required Experience and Skills:

• 5 years of applied data science experience in a banking environment
• Proven track record of deploying production-grade models with ongoing performance management
• Strong hands-on skills in SQL Python and ML libraries (e.g. scikit-learn XGBoost)
• Demonstrated experience in feature engineering and large-scale data handling
• Familiarity with MLOps pipelines and tooling for monitoring and automation
• Strong commercial acumen and ability to scope and deliver high-impact use cases
• Excellent presentation communication and stakeholder engagement skills
• Deep understanding of model governance standards and regulatory expectations

Preferred Qualifications:

• Experience with platforms such as Dataiku and Databricks is a significant bonus
• Strong Retail banking domain knowledge
• Proven experience with statistical modelling mastery
• Bachelors or Masters degree in a quantitative field (e.g. Computer Science Statistics Engineering)

Experience working in cloud environments (Azure AWS or GCP)

Remote Work :

No

Employment Type :

Full-time