We are currently looking for an experienced Data Scientist to join our Data team to be involved in uncovering insights from data, surfacing these insights with best-in-class visualizations that drive decision making, and then going on to implement advanced models that help maximise business value.
As a Data Scientist you'd be working with us as an enabler of quality data-driven decision making. Your work will involve uncovering insights from data, surfacing these insights with best-in-class visualizations to drive decision making, and then going on to implement advanced models that help maximise business value. You will also work with different functional teams across the bank and be a driving force in continuously levelling up our already data-driven culture.
You will work within the data team to continuously ideate and improve our data science infrastructure and build tools that will help democratize the ability to make statistically sound insights/decisions in the bank.
Proficiency using Python and SQL for data analysis and modelling with large data sets.
Experience using business intelligence platforms (eg. Tableau, PowerBI) to visualize, explore and share data.
Experience working with a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and understand their real-world advantages/drawbacks.
General AWS services (or other cloud platform equivalents): Glue, Athena, S3.
Experience applying multivariate experimentation and hypothesis testing.
Exposure to data science environments (eg. AWS Sagemaker, Jupyter, MLFlow).
Data Analysis/ML frameworks and tools: Pandas, Numpy, Sci-Kit Learn, Keras, Pytorch.
Agile/Lean project methodologies and rituals: Scrum, Kanban.
Version control: git commands, branching strategies, collaboration etiquette, documentation best practices.
Workflow scheduling and monitoring tools: Apache Airflow, Luigi, AWS Batch.