AVP- Data Scientist
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Job Description
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
Employment Type
Full-time
