At OneSpan, we specialize in digital identity and anti-fraud solutions that create exceptional and secure experiences. Here Is a Little Taste Of Your Challenge
* Work side by side with the Security team to understand current and emerging attack techniques against mobile apps, and translate them into well-framed data problems.
* Explore and analyze our telemetry (datalake on ClickHouse and S3) to uncover signals, patterns, and anomalies with detection value.
* Design, prototype, and evaluate attack detection and prevention models: anomaly detection, classification with scarce labels, behavioral models, event time-series analysis.
* Define how success is measured: detection metrics, acceptable false-positive rates, and evaluation methodology when complete ground truth doesn't exist.
* Take validated prototypes toward production in collaboration with the engineering team, and lay the foundations (features, datasets, labeling processes) for the team's ML capability to grow.
* Stay current with research in ML applied to security (anomaly detection, adversarial ML, fraud) and bring actionable ideas. Who are you?
* Proven experience (5+ years, or PhD plus applied experience) researching and developing ML models on real-world problems, ideally in security, fraud, anti-bot, anti-malware, or similar adversarial domains.
* Strong command of anomaly detection and unsupervised / semi-supervised learning: labeled data is scarce in this domain, and that shapes the entire approach.
* Experience with large-scale telemetry or event data: logs, time series, user or device behavior data.
* Full autonomy with raw data: solid Python (pandas/polars, scikit-learn, PyTorch or similar),



advanced SQL, and comfort working directly against a datalake without prepared datasets.
* Product mindset: the ability to go from a research idea to an evaluable prototype in weeks, not months, and to communicate results to non-technical stakeholders.
* Genuine curiosity about cybersecurity and the drive to learn the domain in depth alongside the team's experts. Nice to have
* Prior experience in mobile security, EDR, banking anti-fraud, or intrusion detection systems.
* Knowledge of adversarial machine learning (model evasion, robustness).
* Experience with AWS S3, ClickHouse or other high-volume analytical databases.
* Publications, open-source contributions, or talks in applied ML or security.
* Experience deploying models to production (basic MLOps). Practically Speaking, What's In It For You
* The chance to define the company's ML strategy from scratch, with direct product impact and full visibility.
* A technically fascinating problem: a real adversary that evolves, massive untapped data, and freedom to research.
* Daily collaboration with a Security team with deep domain expertise.
* Optimized onboarding – for your optimal start and customized induction.
* Technical ownership – take responsibility for key components and shape how we build our platform.
* Enthusiastic team – waiting to help you succeed and collaborate with experienced engineers.
* Beautiful office spaces – in the heart of Barcelona, 200m from Sants Estació with regular team/community events (breakfast, lunch...)
* Good transport connections – easy to reach by public transport.
* Versátil

working hours
& Hybrid working

📌 Research Scientist (Barcelona)
🏢 OneSpan
📍 Barcelona