Senior Data Scientist – Fraud

Jobtailor; Deutschland; Vor Ort; EUR 90.000 - 120.000; Vollzeit

Zusammenfassung

Jobtailor is seeking a Senior Data Scientist to develop and productionize machine learning models for fraud detection in a fast-paced fintech environment. You will own monitoring and retraining cycles and work across teams to implement real-world mitigations.

You bring 5+ years in data science or risk analytics, strong statistics, Python and SQL skills, and a talent for turning data insights into actionable fraud controls. This role offers collaborative culture and impactful work in Germany.

Qualifikationen

• A strong foundation in statistics with a degree in a quantitative field (Statistics, Mathematics, Engineering, Computer Science, or similar)
• 5+ years of experience in data science, decision science, or risk analytics within fraud, payments, or financial crime
• Hands-on experience building and deploying machine learning models in a production environment, fraud, risk, or financial services experience is a strong plus
• Solid grounding in data science fundamentals: experimentation, statistical inference, model evaluation, and feature engineering
• Proficiency in Python and SQL; comfort working across the full model development lifecycle
• An investigative instinct, you enjoy digging into data to find patterns others miss
• The ability to communicate technical findings clearly to non-technical stakeholders and translate insights into action
• Comfort working in fast-paced, cross-functional teams with high ownership expectations

Aufgaben

• Prototype, evaluate and productionize ML models for fraud detection and monitor retraining cycles.
• Design experiments to measure fraud interventions, balancing customer experience against loss reduction.
• Size fraud typologies across product lines to inform prioritisation and investment decisions.
• Build and maintain anomaly detection systems to surface novel fraud vectors early.
• Collaborate with fraud operations, engineers, product managers, and data analysts to translate model outputs into mitigations.

Kenntnisse

Statistics foundation

Data science experience

Python

SQL

Experimentation

Communication

Ausbildung

Quantitative degree

Tools

ML Deployment

Jobbeschreibung

Responsibilities

• Model Development: Prototype, evaluate, and help productionize machine learning models for fraud detection; own their ongoing monitoring and retraining cycles.
• Experimentation: Design and run experiments to measure the impact of fraud interventions, balancing customer experience against loss reduction.
• Risk Assessment: Size fraud typologies across our product lines to inform prioritisation and investment decisions.
• System Maintenance: Build and maintain anomaly detection systems to surface novel fraud vectors before they scale.
• Cross-Functional Collaboration: Work closely with fraud operations, engineers, product managers, and data analysts to translate model outputs into real-world mitigations.

Requirements

• A strong foundation in statistics with a degree in a quantitative field (Statistics, Mathematics, Engineering, Computer Science, or similar)
• 5+ years of experience in data science, decision science, or risk analytics within fraud, payments, or financial crime
• Hands‑on experience building and deploying machine learning models in a production environment, fraud, risk, or financial services experience is a strong plus
• Solid grounding in data science fundamentals: experimentation, statistical inference, model evaluation, and feature engineering
• Proficiency in Python and SQL; comfort working across the full model development lifecycle
• An investigative instinct, you enjoy digging into data to find patterns others miss
• The ability to communicate technical findings clearly to non-technical stakeholders and translate insights into action
• Comfort working in fast-paced, cross-functional teams with high ownership expectations

Core Competencies

Demonstrates expertise in building and deploying machine learning models for fraud detection, with a strong foundation in statistics and data science fundamentals. Proven ability to collaborate cross-functionally and communicate technical insights effectively to drive actionable outcomes.