Machine Learning Engineer / Applied Scientist

About Ferocia We're the team behind Up , but under the hood, we're Ferocia - a passionate tech company driven by financial inclusion. Since 2011, we've been crafting innovative financial tools, starting with the digital platform for Bendigo Bank. We believe technology can empower everyone, from the advantaged to the disadvantaged, which is why Up was born.

Now, as part of the Bendigo and Adelaide Bank family, we combine the agility of a small company with the reach and stability of a major player. Together, we're carbon neutral , community-focused , and dedicated to high standards of corporate governance . Our mission?

To leverage technology to help Australians move from financial stress and anxiety to a place of confidence and empowerment. Want to join us? We'd love to hear from you!

The role 👤 We're looking for someone who thinks like a scientist and wants to learn to build, automate, and deploy like an engineer. You'll join our Data AI team: a small, diverse group that builds and operates data products across the business. Fraud detection, customer service automation, intelligent query routing, unstructured data analysis, that kind of thing.

Every person on the team brings a different background. What we all share is that we own our work from the initial idea all the way into production. What we're adding with this role is a new lens: quantitative research and behavioural science .

Your understanding of how and why people behave the way they do will strengthen the entire team's ability to work on customer intelligence problems: identifying common attributes of our customers, what drives their behaviours, and how to better engage with them. On a normal day, you could be: Applying causal inference and inferential statistics to figure out what's actually driving customer behaviour vs. what's just correlated with it.

Training, evaluating, and automating models that will integrate directly into our systems as live features. Translating model outputs into strategic recommendations, ensuring that "Customer Intelligence" leads to measurable improvements in experience. Taking existing customer segmentation research and automating it into a scheduled pipeline that can be acted on and used beyond reporting.

Partnering with our Customer Experience and Analytics teams to build the automated models and self-serve tooling that will elevate how the entire company understands and serves our customers. Growing your engineering skills. You bring at least a baseline of clean Python, Git, and SQL; we’ll teach you our engineering standards and best practices to ensure your work scales seamlessly.

Where you'll work: Melbourne (hybrid - in office when it matters, WFH when you need focus time) You should apply if… ↪️ You have: Quantitative research chops. A postgraduate degree (or equivalent through industry experience) in a field where causal inference is core: econometrics, biostatistics, quantitative psychology, computational social science, epidemiology, or similar. You know when a causal claim is defensible and you choose methods based on the question, not the tool.

2-5 years in the industry applying quantitative methods, ideally in a customer-facing or product context. You've produced analysis that actually changed a decision. Strong inferential statistics.

Hypothesis testing, confidence intervals, regression, causal inference methods (diff-in-diff, propensity score matching, instrumental variables). Plus solid ML fundamentals: clustering, classification, predictive modelling and the judgment to know when a simple regression beats a deep learning model. Python and SQL proficiency.

Experience with scikit-learn, pytorch, XGBoost, or similar. You write code someone else can read and maintain. Engineering willingness.

You don't need to be a software engineer today, but you want to become one. You're drawn to teams that build and automate, not teams that just report. You see version control, testing, and code review as growth, not overhead.