About Kasada
Join us in stopping bad bots, for good! Kasada protects millions of online users every day. Founded to stop automated bot attacks, we believe the internet should be a safe place for everyone. Bad bots are highly destructive. They take over accounts, steal content, overload systems, and infrastructure and cause billions of dollars in damages every year. Seeking to restore trust in the internet, Kasada stops bots at the very first request, including those that have never been seen before.
We've grown from a few friends working out of a shipping container under the Sydney Harbour Bridge to now operating globally. We're spread across the world protecting some of the most well‐known brands.
We're an innately curious team that's not afraid to bring bold ideas to create better ways of solving problems. We're looking for people who are passionate about solving some of the most difficult and pressing cybersecurity issues, while having fun doing it!
Kasada has an exciting opportunity to join our team responsible for defending our customers against fraud and malicious automation. As the first Data Scientist in the Account Intelligence team, you will work in a high energy team applying predictive modelling and statistical methods to help us defeat adversaries. A critical part of this role involves partnering with engineering, research, and security operations to turn data science work into production defences that stand up against real attackers.
We are looking for a hands‐on data scientist to help shape the predictive detection capabilities within Account Intelligence and the way we tell legitimate users apart from adversaries.
What you will be doing
* Build predictive defences: Use data and models to support the development of risk mitigation strategies and interventions while preserving and improving the user experience, building, training, and iterating on predictive models that stand up to real adversaries at Kasada scale.
* Evaluate rigorously: Pressure test your own work before anyone else has to. Evaluate model performance and trade‐offs carefully, so detection decisions stand up to scrutiny from engineering, product, and security operations long after the model goes live.
* Partner with engineering, research, and product: Work alongside engineers, researchers, and product managers to take models from notebook to production, integrating them into Kasada's platform and iterating on them as customer needs and attacker behaviour shift.
* Make models legible: Make your models and their outputs understandable to both technical and non‐technical colleagues. Detection decisions should never be a black box to the teams that rely on them, and that bar sits with you.
* Hunt adversarial patterns: Analyse large datasets to surface anomalies, identify adversarial behaviours, and flag emerging attack patterns. Stay current with developments in adversarial ML and cybersecurity, and apply relevant techniques to strengthen our defence capabilities.
What You'll Bring
* Genuinely curious about fraud and the adversarial landscape, and asking the right questions about attacker behaviour, false positives, and the real‐world impact of models on legitimate users.
* Enjoy partnering across engineering, research, and product, and able to explain models and their limitations to teammates and stakeholders who don't share your technical background.
* 2+ years of professional experience in data science or applied ML, with a solid foundation in statistical concepts, sampling, time‐series data, and hands‐on predictive modelling (e.g., gradient boosted trees, random forests, deep learning).
* Proficiency in Python, SQL, and standard ML libraries (e.g., scikit‐learn, PyTorch, TensorFlow).
* Experience evaluating predictive models in production or production‐like settings, including thinking through precision, recall, calibration, and how models behave as attackers adapt.
* Strong problem‐solving and analytical skills, with keen attention to detail and a bias toward pressure testing your own work before shipping.
* Experience working within cloud environments (e.g., AWS).
Additional bonus points if:
* Prior experience in fraud, trust and safety, account takeover, or abuse detection.
* Built or supported ML models in adversarial settings, where attackers actively try to evade or retool against your defences.
What you will be working with
* AWS, ClickHouse
* Python, scikit‐learn, PyTorch, TensorFlow
The Benefits of Being a Kasadian
* Stake in Kasada's global success through equity/stock options.
* Support for growing families, including generous parental leave and resources before, during, and after leave.
* Wellbeing support to help you grow and recharge, including access to our EAP with confidential counselling for you and your loved ones.
* Birthday leave.
* Wellness leave.
* Annual company offsites to connect, collaborate, and celebrate together.
* A dog‐friendly HQ in Sydney.
Please note: Kasada is an E‐Verify employer (US‐based applicants only).
#J-18808-Ljbffr