About your team

We’re a Data team, bringing together specialists passionate about turning data into reliable, actionable decisions . We build the foundations—data pipelines, datasets, and platform capabilities—that make analytics and machine learning scalable and trustworthy for the whole organization.

In this role, you’ll join our Risk-focused efforts and help evolve our fraud detection capabilities. The team is moving from a legacy, manual approach toward an end-to-end ML lifecycle with robust training, deployment, monitoring, and evaluation. You’ll work on real-time decisioning use cases, partner closely with Risk and engineering stakeholders, and tackle the practical challenges of fraud modeling—imbalanced data, delayed/partial labels, and building strong feedback loops to continuously improve model performance.

• Build and iterate on real-time fraud/risk models (e.g., gradient boosting and anomaly detection approaches) to score transactions during checkout and support Risk decisioning.

• Own the full ML lifecycle for fraud detection models: data exploration, feature engineering, training, evaluation, deployment, monitoring, and continuous improvement.

• Design robust evaluation strategies for rare-event, highly imbalanced data, including handling delayed/partial ground truth and defining metrics aligned with business constraints.

• Partner closely with Risk, backend, and Data/Platform teams to productionize models behind an API, integrate with the risk engine, and improve model-driven decision flows (pre-/post-authorization).

• Drive experimentation and feedback-loop initiatives to improve labels and model quality over time, while maintaining strong reliability, observability, and documentation.

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• 3+ years of experience as a Machine Learning Engineer (or in a similar applied ML role), ideally working with risk/fraud, anomaly detection, credit/default modeling, or other rare-event classification problems.

• Strong Python skills and hands-on experience building and iterating on supervised ML models (e.g., Gradient Boosting/LightGBM or similar), including feature engineering and model evaluation.

• Proven ability to design and run robust experimentation and evaluation under real-world constraints (imbalanced data, delayed/late-arriving labels, noisy or partial ground truth).

• Experience taking models to production and supporting the full model lifecycle (training, deployment, monitoring, and iteration) in collaboration with engineering teams.

• Solid knowledge of ML metrics and decisioning (precision/recall, thresholding, calibration, offline vs. online performance) and how they translate into business outcomes.

• Familiarity with modern MLOps tooling and practices (e.g., MLflow) and working with feature stores (Databricks Feature Store or alternatives).

• Nice to have: experience with real-time / streaming feature pipelines or infrastructure (e.g., Kafka, Flink, Feast) and building low-latency model services/APIs for real-time scoring.

What it’s like to work 

*Opportunity to join our Employee Stock Options program.

*Opportunity to help scale a unique product.

*Various bonus systems: performance-based, referral, additional paid leave, personal learning budget.

*Paid volunteering opportunities.

*Work location of your choice: office, remote, opportunity to work and travel.

*Personal and professional growth at an exponential rate supported by well-defined feedback and promotion processes.

*Please attach CV’s in English.

Are you interested in this position?

Apply by clicking on the “Apply Now” button below!
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