Principal Data Scientist & Analytics Lead

National Australia Bank; City of Melbourne; Hybrid; AUD 180,000 - 240,000; Full time

Benefits offered by this job

Enhanced leave options

Wellbeing support

Access to NAB Perks

Ongoing career development

Job summary

National Australia Bank (NAB) is seeking a Principal Data Scientist to lead the Analytics, Fin Crime & Fraud team in Melbourne. This role focuses on delivering high-value data solutions for customer-centric banking through advanced analytics and cloud-based platforms.

You’ll mentor a growing analytics team, drive ML development, and partner with stakeholders to translate complex data challenges into practical outcomes.

Qualifications

• Leading a team of data science professionals and delivering milestones.
• Presenting insights and learnings to stakeholders.
• ETL and data engineering experience including modelling.
• Proficiency in SQL across Oracle, Redshift, AWS Glue, plus Python.
• Experience with Spark EMR and R/SAS.
• CI/CD and Git/GitHub familiarity.
• Cloud platforms such as Databricks, AWS, Azure or GCP.

Responsibilities

• Provide technical leadership to enable a high performing Analytics team.
• Develop and deploy machine learning models.
• Address complex business issues with tactical and strategic approaches.
• Present ideas and outcomes to stakeholders using clear frameworks.
• Engage with data SMEs to collect information from disparate sources.
• Collaborate with analytics partners to provision data and shape insights.
• Help embed automation and cloud-based analytics at NAB.

Tools

Oracle

Redshift

AWS Glue

Spark EMR

R/SAS

Git/GitHub

Databricks

AWS

Azure

GCP

Job description

National Australia Bank (NAB) is seeking a Principal Data Scientist to lead the Analytics, Fin Crime & Fraud team in Melbourne. This role focuses on delivering high-value data solutions for customer-centric banking through advanced analytics and cloud-based platforms.

You’ll mentor a growing analytics team, drive ML development, and partner with stakeholders to translate complex data challenges into practical outcomes.