As a Data Scientist in our team, you will be instrumental in solving real-world business problems using advanced data science techniques and cloud-native technologies. You’ll work across diverse datasets, build scalable tools, and deploy solutions using AWS services including SageMaker, Glue, EMR, and Redshift. Key Responsibilities Source, clean, and prepare large-scale datasets for analytics and operational use. Create productivity tools using Python and R scripts to streamline workflows and enhance the effective use of available models. Apply statistical and analytical techniques to extract insights and support decision-making. Build and deploy data-driven solutions using AWS SageMaker, leveraging asynchronous inference and batch transform pipelines. Collaborate with stakeholders to understand business priorities and translate them into data-driven solutions. Create clear and compelling visualisations and presentations for technical and non-technical audiences. Ensure responsible use of AI technologies by adhering to governance and ethical standards. Contribute to CI/CD pipelines for solution deployment and monitoring. Preferred Skills & Experience Proficiency in Python, R, SQL, and experience with Spark or Snowflake. Hands-on experience with AWS services: SageMaker, Glue, EMR, S3, Redshift, DocumentDB. Familiarity with MLOps practices and model lifecycle management. Strong understanding of data engineering principles and cloud architecture. Exposure to frameworks and design concepts, with an understanding of how to treat data as a product . Experience in GenAI, prompt engineering, and responsible AI guardrails is a plus. While building predictive models is not a core responsibility, having that skill set is considered an added advantage. Excellent communication and stakeholder engagement skills. Domain Expertise Experience working with data across the banking and financial services domain , including products from Retail, Institutional, and Business Banking . #J-18808-Ljbffr