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: Data Scientist

Location: Bangalore

Business & Team:

At the heart of the Chief Operations Office, we're reimagining how we serve customers in operations through the power of transformative AI solutions.

As a Data Scientist reporting to a Chapter Lead of Data Science in COO, you'll be a hands-on data scientist working in a cross-functional squad with engineers, senior data scientists and other experts to help design, build and deliver responsible AI solutions.

Impact & contribution:

You'll contribute to machine learning, Generative AI and agentic AI initiatives by building well-defined solution components, preparing and analysing data, evaluating model outcomes, and supporting deployment and monitoring activities.

You'll continue to develop your technical expertise while applying emerging AI techniques to real customers, colleagues and business problems.

If you're passionate about applying AI to meaningful business problems and building your data science capability, we'd love to hear from you.

Roles & Responsibilities:

• GenAI and Agentic systems: Support the prototype, development, testing, deployment and monitoring of GenAI and agentic AI solution components, under the guidance of senior team members. Contribute to prompt engineering, retrieval augmentation, evaluation and guardrail testing activities.
• Machine Learning Models: Develop, test and evaluate machine learning models for defined business problems, including feature engineering, model selection, model evaluation and performance improvement. Support deployment and monitoring of models in production environments.
• Data Preparation and Analysis: Source, clean, transform and analyze structured and unstructured data to support model building, experimentation and data science delivery. Apply strong attention to detail in data quality checks, validation and documentation.
• Production Delivery and Monitoring: Complete tasks associated with deploying, monitoring and maintaining models or AI components in production, following established engineering, risk and governance standards.
• Drive Continuous Improvement and Business Outcomes: Work closely with operations teams and squad members to understand business needs, test ideas, share findings and contribute to AI and GenAI capabilities that improve customer and colleague experiences.

Essential Skills:

Technical Collaboration and Continuous Applied Learning:

• 5+ years of proven experience in the field of data science
• Experience collaborating with software engineers, senior data scientists, business stakeholders and squad members to develop reliable data science, machine learning or AI solutions.
• Applied learning mindset, with curiosity to explore emerging AI patterns, test ideas and demonstrate value in a practical business and customer context.
• Ability to explain analysis, model outputs, risks and technical trade-offs clearly to technical and non-technical audiences.

Technical Skills and Tools:

• Generative AI: Experience applying Generative AI techniques such as prompt engineering, Retrieval-Augmented Generation (RAG), evaluation frameworks and guardrail testing. Exposure to orchestration patterns and tools such as LangChain, LangGraph or similar frameworks is desirable.
• Machine Learning: Experience developing machine learning models, including natural language processing, classification and clustering techniques. Understanding of feature engineering, model evaluation, model improvement and production deployment concepts.
• Cutting-Edge AI: Interest in working on projects that use traditional AI, Generative AI and agentic AI, with the ability to contribute to new approaches as part of a cross-functional team.
• Version Control and CI/CD: Experience using version control tools such as GitHub and awareness of CI/CD practices that support reliable production delivery.
• Tooling: Hands-on experience with data science tools such as Python, SQL and Spark. Exposure to R, TensorFlow, PyTorch or similar frameworks is desirable.
• Data Engineering: Knowledge of ETL processes, data pipelines and data integration concepts to support efficient data flow and model-ready datasets.
• Deployment: Familiarity with Docker or similar containerization approaches for packaging and deploying data science or AI components.
• Cloud Architecture: Foundational understanding of AWS or Azure cloud architecture and integration, with exposure to cloud-based data science, AI or analytics solutions.
• Responsible AI and Governance: Awareness of privacy, security, model risk, monitoring, responsible AI and governance expectations, with the ability to follow established standards and controls.

Education Qualification:

Bachelor’s degree or master’s degree in engineering in Computer Science/Information Technology.