Data Scientist

At ANZ, were shaping a world where people and communities thrive, driven by a common goal: to improve the financial wellbeing and sustainability of our millions of customers.

The Data Scientist role within the Business Reporting and Customer Analytics squad will aid in predictive analytics, and models to bring business values to drive Retail Business strategies across Classic and Plus customer initiatives, building deep dive insights & analytics. These insights will be integrated into customer engagement strategies across Retail Customers. The role involves understanding retail data and customer behaviour and building algorithms. Collaboration with behavioural scientists is key to incorporating psychological and demographic factors into the models.

This position will play a critical role in streamlining the analytics across Retail Divisional analytics and in collaborating with the Product streams to make sure scalable solutions are built that can be leveraged across Product streams. This role is also responsible to drive the best practices and standards across Retail Analytics tribe, and to contribute to the BI simplification journey.

Working with a team of data scientists, engineers, and analysts, the Data Scientist will apply statistical and analytical skills to a wide range of data to create customer-centric solutions. The role also involves communicating insights through model outcomes, data visualization and storytelling, supporting data-driven decision-making across the organization.

• Address and solve complex business issues using substantial amounts of data with an analytical mindset.
• Have a clear understanding of the end-to-end analytics journey as well as define and deliver the Tribe data roadmap.
• Use large datasets to identify insights, design sophisticated algorithms, and drive strategic solutions that have a measurable impact on the organization, improving business processes and automating decisions.
• Design, build, and deploy models tailored to customers, products, and business channels, continuously evaluating their performance to maintain relevance and effectiveness.
• Drive the development and implementation of cutting-edge data science capabilities, leading the creation of tools and methodologies that profile customer segments and optimize business performance, while supporting data platforms and decision delivery tools.
• Continuous generation, monitoring, presenting, and conducting of analysis of relevant customer insights to use at any time for the development and improvement of relevant and innovative propositions.
• Continuous measurement and analysis of customer journeys.
• Generate, monitor, present and analyse relevant customer insights, costs, benefits, and risks for customers, clients, and cohorts; this could include business casing, what if scenarios and opportunity sizing & assessments, and development and maintenance of dashboards and visualisations.
• Sourcing data from a variety of sources to combine, synthesise and analyse to generate insights.
  

• A collaborator with effective communication and presentation skills.
• Good understanding of concepts in applied probability and statistics, advanced analytics.
• A solid understanding of the banking sector, including knowledge of products, services, channels, and the associated data structures and dynamics.
• Deep knowledge of core statistical concepts such as probability, inferential statistics, sampling, regression, power and test, analysis of variance. Hands-on experience with predictive modeling, pattern recognition, clustering, and both supervised and unsupervised learning algorithms.
• Expertise in leading comprehensive EDA exercises to uncover key patterns, trends, and anomalies within complex datasets, applying various statistical techniques and visualization tools to generate actionable insights.
• A minimum of 5-8 years of experience in predictive modelling, with a strong focus on financial services or digital banking preferred. Proficiency in building models that directly impact business decision-making.
• Relevant years experience with tools and techniques for analysis, data manipulation and presentation (e.g., SQL, Tableau, QlikSense, Python)
• Good knowledge in OpenShift platforms and tools (Airflow, DBT, GitHub, Starburst)
• Good knowledge in GCP concepts and BQ
• Strong data visualisation skills with ability to translate data insights into practical business recommendations.
• Ability to effectively communicate to all stakeholders (technical and non-technical).
  

9th Dec 2024, 11.59pm, (Melbourne Australia)