Data Scientist

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 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.