Senior Data Scientist (cba)

Job Description

Organization: At CommBank, we never lose sight of the role we play in other people’s financial wellbeing. Our focus is to help people and businesses move forward to progress. To make the right financial decisions and achieve their dreams, targets, and aspirations. Regardless of where you work within our organisation, your initiative, talent, ideas, and energy all contribute to the impact that we can make with our work. Together we can achieve great things.

Job Title: Senior Data Scientist
Location: Bengaluru - Manyata Tech Park

Business & Team: The Chief Data and Analytics Office (CDAO) group delivers the groups data and AI solutions to ensure the highest levels of customer service through excellent insights, process excellence and AI innovation. We deliver available, reliable, consistent technology solutions for our internal and external customers, essential tools for our people and innovative product platforms for our customers across the bank.
Our team is composed of data and Analytics engineers, data scientists, and leaders, who bring in the right mix of skills. We also work very closely with our business and operations colleagues to support these services which are critical to the Australian and Global economy.

Impact & Contribution: As a Senior Data Scientist, you will play a key role in the design, development, and deployment of state-of-the-art AI models in different areas in Retail Banking. In this role you will lead and mentor a team of talented data scientists to deliver high quality solutions leveraging machine learning, statistical modelling, and generative AI. You will also be supporting the uplift of AI capability across the group, empowering teams to deliver best outcomes for our customers and business. This role involves close collaboration with stakeholders, product owners, engineers, and other teams to develop and implement best in class AI solutions supporting group strategy.

Roles & Responsibilities:

• Create conversational chatbots using foundational LLM models. Focus on improving Retrieval-Augmented Generation (RAG) accuracy to enhance the performance of AI models.
• Utilize experience with Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE) to build innovative AI solutions.
• Analyze data, build models, and develop algorithms to identify patterns and anomalies that may indicate potential disputes or risks.
• Work closely with stakeholders, including banking professionals and customers, to understand their requirements, gather feedback, and ensure that AI solutions meet their needs.
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• Lead and drive data science projects independently, owning the end-to-end process from data collection to model deployment, including metrics reporting for performance of LLMs
• Apply advanced statistical and predictive modelling techniques to build, maintain, and improve on multiple real-time decision systems.
• Collaborate with business and cross-functional teams to understand their needs and implement data-driven solutions.
• Mentor junior data scientists and provide guidance on their projects.
• Present complex results to non-technical stakeholders, translating data insights into actionable business strategies.
• Stay abreast of emerging technologies and industry trends, continuously enhancing expertise in data science methodologies and tools.
• Drive innovation by exploring new approaches and techniques for solving complex business problems through data analysis and modelling.
• Strongly support the adoption of data science across the organization.
• Contribute to the strategic planning and direction of data science initiatives within the organisation.

Essential Skills:

• 9-12 years of extensive experience in the field with a demonstrated history of leading and managing complex AI and machine learning projects.
• Strong proficiency in programming languages such as Python, R, and C++, as well as deep knowledge of machine learning frameworks like TensorFlow, PyTorch, or similar.
• Ability to write well designed, modular and optimized code.
• Knowledge of Snowflake, AWS, Azure etc.
• Knowledge of commonly used data structures and algorithms.
• Solid foundation of Statistics and core ML algorithms at a mathematical (under the hood) level.
• Proven expertise in developing state-of-the-art algorithms for predictive analytics, natural language processing, computer vision, or other AI domains.
• Knowledge of Time Series, NLP and Deep Learning and Generative AI is preferred.
• Knowledge and hands-on experience in developing solutions with Large Language Models.
• Have experience in Prompt Engineering / Platform Engineering / Knowledge Engineering disciplines.
• Must have been part of projects building and deploying predictive models in production (financial services domain preferred) involving large and complex data sets.
• Experience in Data Science in Sales and Marketing, Campaign Analytics, Ecommerce Retail or banking products for retail or business banking is preferred.
• Proven expertise in developing state-of-the-art algorithms for predictive analytics, natural language processing, computer vision, or other AI domains.
• Understanding of principles of software engineering; Built and deployed large scale software applications.
• Ability to communicate clearly and effectively.
• Demonstrated expertise through blogposts, research, participation in competitions, speaking opportunities, patents and paper publications.
• Most importantly - ability to identify and translate theories into real applications to solve practical problems.
• Exposure to tools/services/capabilities like:
  • Cloud Solution Architecture/Development/Security/IaaC (AWS + Azure)
  • Scripting tools: Python / TypeScript / C#
  • Gen AI frameworks & tools (e.g. Langchain, Semantic Kernel)

Education Qualification: Bachelor’s degree or Master’s degree in engineering or Ph.D. in Data Science, Machine Learning, Computer Science, Computational Linguistics, Statistics, Mathematics, or Physics.