Senior/Lead Data Scientist-Fraud
$63,236–$90,429 year
On-siteMilan, Lombardy, Italy
Job Summary
Build best-in-class machine learning systems from the ground up, owning the complete pipeline from raw data through feature engineering, model design, training, and real-time production deployment. Convert existing rules-based fraud systems into sophisticated, model-driven architectures operating at scale across hundreds of millions of transactions. Construct infrastructure from scratch rather than maintaining frameworks, translating ambiguous business problems into precise technical solutions using graph networks, anomaly detection, and behavioral signals. This role requires strong Python and SQL skills with experience in scikit-learn, LightGBM, Docker, and Jenkins, alongside a degree in a quantitative field. Candidates must demonstrate proven track records of building end-to-end ML systems from scratch, including real-time inference and large-scale dataset handling. Familiarity with AWS and CI/CD practices is advantageous.
Required Qualifications
- End-to-end ML ownership across the full stack: data engineering, feature development, model design, training, low-latency production deployment, monitoring, and retraining
- Strong instinct for when a model is ready for production and when it is not
- Proven track record of building ML models and pipelines from scratch, not integrating or extending someone else's product or tooling
- Experienced building real-time or near-real-time inference systems
- Comfortable with large-scale datasets including hundreds of millions of transactions and high-dimensional feature spaces
- Strong Python and SQL skills with hands-on experience in scikit-learn, LightGBM, Docker, Jenkins, and modern Python packaging
- Self-motivated, fast-moving, and creative
- Communicates precisely across technical and non-technical audiences including senior stakeholders
- Degree in computer science, physics, applied mathematics, astrophysics, automatic control, mathematics, software engineering, electrical engineering, or a related quantitative field
Desired Qualifications
- Experience building end-to-end ML systems in early-stage startups or small greenfield teams
- Hands-on production experience with graph neural networks, anomaly detection, or behavioural biometrics, beyond prototyping or fine-tuning
- Familiarity with AWS (SageMaker, Lambda, S3, Athena) and CI/CD practices
- Experience mentoring or technically guiding other data scientists
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