Senior Data Scientist

Job Description Roles & Responsibilities Key Accountabilities 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) Requirements 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) Qualifications & 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 Desired Candidate Profile 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 Company Industry BankingFinancial ServicesBroking Department / Functional Area IT Software Keywords Senior Data Scientist Get real-time job updates only on our App