Inactive
W

Westpac

Banking and financial services for customers

Data Scientist - DDAI

Data SciencePosted on Aug 28
Full-timeAustraliaNo salary listed

About the Job

Requirements

  • Proven ability to apply real-time data science solutions to commercial and business problems, with evidence that solutions moved beyond analysis into use.
  • Advanced Python and SQL skills, including data manipulation, modelling, testing and reproducible workflows.
  • Sound knowledge of statistics, experiment design, machine learning evaluation and common failure modes.
  • Experience with libraries such as scikit-learn, XGBoost, TensorFlow, PyTorch or equivalent, selected according to the problem.
  • Exposure to Azure Machine Learning, Azure AI services, Azure OpenAI, Databricks, Dataiku or comparable cloud platforms is valuable.
  • Practical understanding of version control, APIs, data pipelines, CI/CD, model monitoring and MLOps concepts.
  • Ability to explain what was built, why the approach was chosen, how it performed and what business outcome it enabled.
  • Commitment to responsible, secure and explainable use of data and AI in a regulated environment.
  • Ability to own defined data science use cases from problem framing and data exploration through modelling, validation and implementation support.
  • Ability to build robust features, experiments and evaluation frameworks using Python and SQL.
  • Ability to apply classical machine learning, statistical modelling, forecasting, optimisation, natural language processing, generative AI or other methods when they fit the problem.
  • Ability to work with engineers to package models, expose services, integrate pipelines and establish monitoring.
  • Ability to use cloud services and approved platform patterns across Azure and connected hybrid environments.
  • Ability to write clean, testable and reviewable code using Git and modern team delivery practices.
  • Ability to assess bias, explainability, privacy, security, data quality and model risk throughout the lifecycle.
  • Ability to communicate recommendations and uncertainty clearly to technical and non-technical audiences.
  • Ability to measure performance after release and iterate based on evidence, user feedback and changing conditions.

Responsibilities

  • Deliver models and insights that improve customer, risk, productivity or operational outcomes.
  • Help business partners move from broad ambition to testable hypotheses and measurable success criteria.
  • Increase adoption by making model behaviour, trade-offs and limitations understandable.
  • Contribute reusable code, evaluation patterns and delivery assets that make subsequent use cases faster and safer.
  • Work across discovery, experimentation, validation and deployment to create predictive models, forecasting capabilities, optimisation approaches, natural language processing workflows, generative AI applications or decision-support tools.
  • Collaborate with engineering and platform specialists across the hybrid cloud environment, using the Microsoft-aligned stack and selecting fit-for-purpose methods.
  • Own defined data science use cases from problem framing and data exploration through modelling, validation and implementation support.
  • Work with engineers to package models, expose services, integrate pipelines and establish monitoring.
  • Assess bias, explainability, privacy, security, data quality and model risk throughout the lifecycle.
  • Measure performance after release and iterate based on evidence, user feedback and changing conditions.

About the Company

Westpac

Banking and financial services for customers

Westpac is an Australian bank offering a wide range of financial services for individuals and businesses, including everyday banking, loans, payments, and digital banking.

Company size
10,001+
Company stage
IPO
Headquarters
Sydney NSW, Australia
Founded
1817

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