Chapter Lead Decision Science | Sydney, Au

vor 1 Woche


Sydney, Österreich Efinancialcareers Ltd. Vollzeit

Chapter Lead Decision Science Commonwealth Bank of Australia, Sydney, Australia

Do work that matters
As a Data Science Chapter Lead within the Retail Banking AI and Data Science Team, you will lead a team to support the development and management of AI and Machine Learning for the business stakeholders who are serving Retail customers of the bank. This role involves leading and mentoring a team of data science and statistical modelling professionals to navigate the business and deliver high quality modelling solutions which help improve outcomes for our customers. This includes areas such as Machine Learning and Generative AI where our models help uplift customer experience through the use of AI and ML capacity - and more generally any area where modelling and AI can add value to customer outcomes. You'll work closely with key stakeholders, such as the Portfolio teams, Finance and Group to ensure modelling outcomes are accurate and are in alignment to the strategic direction of the business.

Our team culture is centred on collaboration, growth, and learning, sharing new ideas and innovation. We are passionate about data & modelling, and we work together to creatively problem solve a wide variety of challenges that all adhere to our core principles around data quality and reuse.

See yourself in the team
The Retail Banking Services (RBS) AI team is responsible for leading the delivery of world-capabilities to improve the financial well-being of our customers and communities. We support the multiple businesses within RBS area through the development and maintenance of predictive models, creating insights to enable business decision making at a strategic and tactical level.

Role and Responsibilities:
You will be responsible for the development and management of key modelling assets within the team, with an objective to foster a team vision and approach that utilises state of the art data science and statistical techniques. You will be expected to drive higher quality/efficient use of data through continuous process improvements.

More specifically this will include: Lead, mentor and guide junior data scientists in the team to deliver projects and outcomes for the business. As well as leading and mentoring, you'll be expected to make individual hands-on contributions which help drive innovation for the team. Present solutions to senior business and non-technical stakeholders. Utilise data science and machine learning techniques in Python or R to drive model development to solve key business problems. Collaborate with key business areas, including Product and Pricing, Line 1 risk to provide implementable modelling and AI solutions that drive customer experience, profitable growth and productivity. Work closely with colleagues in the broader Data Science community to create a centre of excellence. We want to hear from you if you have: Excellent written & verbal communication skills are required in this role to coordinate and influence senior cross-functional stakeholders and contribute to an empowered high performing team. Exceptional statistical / analytical / data science knowledge with the ability to work in both theoretical and practical analysis spaces. Advanced technical skills - ability to generate actionable value-add insights by understanding complex data and using tools such as Python, R, Spark, Teradata SQL. A relevant tertiary qualification e.g. Mathematics, Statistics, Economics, Econometrics, Computer Science or Engineering. Working with us:
Our people bring their diverse backgrounds and unique perspectives to build a respectful, inclusive, and flexible workplace with an ultimate flexibility to work from any of our engineering hubs within Australia. One where we're driven by our values, and supported to share ideas, initiatives, and energy. One where making a positive impact for customers, communities and each other is part of our every day.

If this sounds like you then apply today Advertising End Date: 26/11/2024

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