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!

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📌 ML Scientist (Barcelona)
🏢 OneSpan
📍 Barcelona