16.09.2025 aktualisiert
Premiumkunde
100 % verfügbarData Scientist / Data Engineer mit Domain Expertise in Credit/Fraud Risk, Pricing
Berlin, Deutschland
Deutschland +2
Dr. in Economicsinfo: Deutschland, Österreich, Schweiz
Skills
dbtSql Data WarehouseApache AirflowAmazon Web ServicesData AnalysisAtlassian JiraAutomatisierungBankingBusiness IntelligenceCloud ComputingInformation EngineeringETLDatenmodellFinancial Risk ManagementForecasting
Data
Data Analytics / Data Engineering
Several years of experience building data teams. Build and maintain DWH, reporting and analytics, ETL pipelines, data modelling, BI.
Data Science
Prediction models and machine learning including cloud deployment and provision to stakeholders.
Domain Knowledge
Credit Risk
Scorecard and prediction model development for credit risk, dunning, collection (application scorecards, behavioral scorecards). Probability of Default (PD), Exposure at Default (EAD), Loss Given Default (LGD), Risk-Adjusted Pricing for term loans, credit lines, credit cards, limit management, debt collection.
Fraud Detection
Machine learning models to predict fraud probability including data and ML pipelines. (supervised machine learning, anomaly detection, outlier detection)
Misc
Pricing, bonus- and incentive systems, A/B-testing and experimental design, automation, optimisation
Skills/Tools
Programming
Python, R, Julia, SQL, Shell, LateX
(basic knowledge: Go, Dart, Rust)
Tools/Technologien
Git, BI Tools (Metabase, PowerBI...), Docker, AWS, Jira, REST API
Methods
Statistics, machine learning,forecasting/time series analysis, survival analysis, simulations, game theory
Data Analytics / Data Engineering
Several years of experience building data teams. Build and maintain DWH, reporting and analytics, ETL pipelines, data modelling, BI.
Data Science
Prediction models and machine learning including cloud deployment and provision to stakeholders.
Domain Knowledge
Credit Risk
Scorecard and prediction model development for credit risk, dunning, collection (application scorecards, behavioral scorecards). Probability of Default (PD), Exposure at Default (EAD), Loss Given Default (LGD), Risk-Adjusted Pricing for term loans, credit lines, credit cards, limit management, debt collection.
Fraud Detection
Machine learning models to predict fraud probability including data and ML pipelines. (supervised machine learning, anomaly detection, outlier detection)
Misc
Pricing, bonus- and incentive systems, A/B-testing and experimental design, automation, optimisation
Skills/Tools
Programming
Python, R, Julia, SQL, Shell, LateX
(basic knowledge: Go, Dart, Rust)
Tools/Technologien
Git, BI Tools (Metabase, PowerBI...), Docker, AWS, Jira, REST API
Methods
Statistics, machine learning,forecasting/time series analysis, survival analysis, simulations, game theory
Erfahrung
Berufserfahrung5 - 10 Jahre
Freelancer seit2025
Sprachen
DeutschMutterspracheEnglischverhandlungssicherSpanischGrundkenntnisse
Projekthistorie
Aufbau von Betrugserkennung und Präventionssystemen sowie Kreditrisiko-Scorecards (Anwendungs- und Verhaltensbasiert). Bewertung von Preisen und Marktrisiken im Elektromobilitätssektor.
Erstellung von Reporting-Strukturen, einschließlich Aufbau und Wartung des Data Warehouse, Business Intelligence, Alerts und Monitoring. Abdeckung von Risikomodellierung, Portfolio-Monitoring und Betrugserkennung unter Einsatz von Machine Learning und Simulationen.
Modellierung der Partnereffizienz im Marketing einschließlich PD/LGD-Vorhersage mittels Statistik und maschinellem Lernen. Anwendung von Betrugserkennung mittels Ausreißer-/Anomalieerkennung.