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.
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