Senior Data Scientist
Skills
About This Role
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 engagements
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 communications
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)
Technical Skills
- Python for data analysis and model development (pandas, scikit-learn, XGBoost, etc.)
- Advanced SQL skills, including stored procedures, window functions, temporary tables, and recursive queries
- Experience with data visualization and reporting tools
- Familiarity with Git (GitHub/GitLab) for version control
- Basic understanding of Spark for large-scale data processing
- Awareness of MLOps practices and model deployment concepts (MLflow, TFX)
& Experience
- Masteru2019s degree or PhD in Finance, Economics, Statistics, Mathematics, or a quantitative field (strongly preferred)
- 8+ years of experience in data science or quantitative analysis roles
- Minimum 5 years of experience in the banking or financial services industry (mandatory)
- Proven track record of delivering ML models in pricing, propensity, or recommendation domains
- Background in Risk, Finance, or quantitative banking functions preferred
- Experience with model validation, governance, and regulatory requirements in financial services
- Professional certifications in Risk (FRM, PRM) or Finance (CFA) are a plus
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