Senior Data Scientist

2 weeks ago

Haymarket, New South Wales, Australia Shift Full time
Overview
Who is Shift?
At Shift, we're business specialists dedicated to helping Australian SMEs take control of their cashflow, streamline trade terms and choose the right financial products.
We believe Australian businesses are the driving force behind our economy and are core to our communities. That's why our business expertise, focus on relationships, and market-leading technology is at the core of everything we do.
Our unique approach to product innovation combined with our collaborative culture means you can build your career in a supportive environment. You'll be joining a diverse team of over 250 people who are always looking to deliver better outcomes for Australian businesses.
About the role
Join Shift as a Senior Data Scientist and drive the future of our analytics capabilities. You'll work closely with leadership, product and risk teams to build and deploy advanced statistical and machine learning models that power credit risk assessment, fraud detection, and profitability strategies. Your expertise will directly shape business decisions, optimise credit processes and elevate the team through mentorship and innovation.
What you\'ll do
Partner with executive, product, risk, and frontline teams to define analytical goals and manage projects end-to-end, ensuring business alignment and executive buy-in
Design, develop, and validate a comprehensive suite of models including credit risk (PD, LGD, EAD), provisioning (AASB/IFRS9), fraud, servicing, and profitability
Translate complex statistical concepts into clear business insights and communicate effectively across stakeholders
Optimize credit strategies, policies, automation, and decision-making through robust modelling and reporting
Continuously improve machine learning architecture and research new techniques to drive innovation and performance
Develop and deploy models in Python, collaborating with technology teams to ensure production integrity
Validate and maintain the integrity of diverse data sources such as

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