AI Engineer
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Key skills for this role
About the Role
Develop machine learning models for banking applications, conduct data analysis, and collaborate with stakeholders while ensuring compliance and utilizing cloud technologies.
Key Skills for This Role
Full Job Posting
Machine Learning Model Development
- Design and develop machine learning models for pricing optimization, including dynamic pricing, rate optimization, and fee structures
- Build propensity models for customer behavior prediction, including churn, cross-sell, upsell, and product adoption
- Develop recommendation systems for personalized product offerings, next-best-action, and customer engagement
Banking Domain Application
- Apply deep banking domain knowledge to frame business problems as machine learning solutions with measurable outcomes
- Partner with Risk, Finance, and business units to identify high-value modelling opportunities
- Ensure models incorporate relevant regulatory requirements, risk considerations, and business constraints
Analysis & Insights
- Conduct exploratory data analysis to identify patterns, relationships, and modelling opportunities in banking data.
- Translate model outputs into actionable business recommendations and insights
- Develop model performance metrics aligned with business KPIs and financial outcomes
- Create data visualizations and reports for stakeholder communication
Prototyping & Delivery
- Develop working prototypes in Python demonstrating model functionality and business value
- Create clear documentation of model methodology, assumptions, limitations, and use cases
- Collaborate with ML Engineers and AI Engineers to transition prototypes into production systems
Stakeholder Collaboration & Governance
- Partner with business stakeholders to understand requirements and validate model outputs
- Present model results, methodology, and recommendations to senior management
- Contribute to model governance, validation, and documentation requirements
- Ensure compliance with data policies, ethical standards, and regulatory requirements
Machine Learning & Statistics
- Expert knowledge of supervised and unsupervised learning techniques for classification, regression, and clustering
- Deep experience with pricing models, propensity modelling, and recommendation systems
- Strong foundation in statistical analysis, hypothesis testing, and experimental design
- Familiarity with deep learning frameworks such as TensorFlow and PyTorch
Banking Domain Expertise
- Comprehensive understanding of banking products (Retail or Corporate), services, and customer lifecycle
- Knowledge of risk functions, including credit risk, market risk, and operational risk frameworks
- Understanding of Finance functions, including P&L drivers, cost allocation, and profitability analysis
- Familiarity with regulatory requirements impacting model development (e.g., IFRS 9, Basel)
Communication & Collaboration
- Ability to translate complex analytical concepts into business language for non-technical stakeholders
- Strong executive-level presentation skills
- Experience working with cross-functional business and technology teams
- Experience with Agile methodologies (Kanban, Scrum)
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