Data Scientist

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• Support the development and enhancement of machine learning and predictive analytics models addressing business requirements across Retail Banking and other lines of business.
  
• Perform data extraction, preparation, cleansing, transformation and exploratory data analysis on large banking datasets.
  
• Support feature engineering, model training, testing, validation and performance optimization.
  
• Evaluate models using appropriate statistical and machine learning performance metrics.
  
• Support the monitoring of deployed models, including model performance, business conversion and effectiveness over time.
  
• Prepare clear analysis, visualizations and presentations to communicate findings and model results to both technical and business stakeholders.
  
• Support integration of analytical and ML outputs into business processes, campaigns, dashboards and operational systems.
  
• Assist in developing and testing Generative AI, LLM and Agentic AI use cases, where applicable.
  
• Research new data science, AI and machine learning techniques and assess their potential applicability to banking use cases.
  
• Maintain proper documentation of datasets, methodologies, models, assumptions, results and implemented solutions.
  
• Follow applicable data governance, information security, model governance and responsible AI requirements.
  

Requirements

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• Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, Statistics, Mathematics or a related quantitative discipline.
  
• 0–2 years of relevant experience in Data Science, Machine Learning, Advanced Analytics or a related field. Strong fresh graduates may also be considered.
  
• Good understanding of fundamental statistics, machine learning and data analysis concepts.
  
• Academic projects, internships or practical experience involving machine learning or data analytics are highly desirable.
  
• Good knowledge of Python for data analysis and machine learning.
  
• Good knowledge of SQL and working with relational databases.
  
• Experience with common Python data science libraries such as Pandas, NumPy, etc.
  
• Understanding of supervised and unsupervised machine learning techniques.
  
• Understanding of model evaluation techniques and common performance metrics.
  
• Ability to perform exploratory data analysis and communicate findings clearly.
  
• Ability to understand business problems and translate them into analytical approaches.
  
• Ability to work collaboratively within a multidisciplinary Data & AI team.
  

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