ANZ Off-Campus Hiring Data Scientist -

Role: Data Scientist
Req ID: 71302
Department: AR Strategic Delivery Australia Data
Division: Australia Retail
Location: Bengaluru

About Us:
At ANZ, we’re leveraging technology and data in innovative ways to enhance the financial well-being and sustainability of our millions of customers.

About the Role:
As a Data Scientist in the Australia Data team, you will play a crucial role in developing advanced analytics and data science solutions across various business domains. You will have the opportunity to build solutions from the ground up, manage end-to-end processes, interact with stakeholders, and engage in the entire product lifecycle. This role offers exposure to emerging technologies and continuous learning opportunities.

Responsibilities:

Address and resolve complex business challenges using large datasets.
Develop insights and algorithms to improve key business processes and automate decision-making.
Continuously generate, monitor, and present fact-based analysis, insights, and models for enhancing innovative propositions.
Design, build, deploy, monitor, and evaluate models for customers, clients, products, and channels.
Initiate, design, and implement innovative capabilities in data science and analytics.

As a Data Scientist, you will:

Demonstrate strong analytical skills to analyze data and derive insights.
Develop data science solutions for diverse business use cases.
Apply advanced analytics, machine learning (ML), and artificial intelligence (AI) to deliver high-quality results.
Continuously improve your technical skills and exhibit strong data management capabilities.
Enhance your communication and presentation skills.
Lead and motivate a team.
Have a solid understanding of banking systems, products, services, and channels.
Effectively communicate with both technical and non-technical stakeholders.

What You Will Bring:

Problem Solving: A deep understanding of advanced analytical techniques and problem-solving methods.
Continuous Improvement & Change: Adaptability to change, supporting and contributing to change projects.
Data Wrangling: Proficiency in data cleaning, transformation, and pre-processing for handling unstructured data.
Programming Skills: Expertise in programming languages like Python and SQL for data manipulation, analysis, and model development.
Technical Skills: Knowledge of machine learning algorithms and techniques such as regression, classification, clustering, and time series forecasting for building predictive models and extracting insights.
Statistical Methods: Understanding of statistical methods and their application for business problem-solving.
Experience: Ability to build statistical models from scratch, including problem definition, data extraction, exploratory data analysis (EDA), model selection/training/evaluation/tuning, validation, interpretation, deployment, and monitoring.
Influence & Relationship Building: Use empathy and understanding to build and maintain relationships and gain buy-in from stakeholders.
Product Expertise: Awareness of new and emerging analytical techniques and capabilities.