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The Risk Data Scientist's job is to build the model-driven intelligence layer that replaces static rules with adaptive, evidence-based decisioning across three risk domains: merchant underwriting and approval optimization, real-time transaction fraud and anomaly detection, and AML/transaction monitoring and SAR prioritization. You will own the full modeling lifecycle - from problem framing and feature engineering to training, validation, deployment, monitoring, and regulatory governance. This is applied ML in a high-stakes, regulated financial context. Models you build will directly determine approval rates, fraud loss rates, chargeback exposure, and SAR filing quality. They will be scrutinized by bank sponsors, card networks, and regulators. The work demands both technical rigor and regulatory fluency: someone who understands why a gradient boosting ensemble outperforms logistic regression on imbalanced fraud data AND why SHAP explainability is a compliance requirement under ECOA adverse action rules. Forward is early in this journey. The person who takes this role will define the ML architecture, build the model governance framework, and set the standard for how Forward uses machine learning in regulated financial services. Key Responsibilities Merchant Risk: Underwriting and Approval Rate Optimization Build and own the merchant risk scoring model - the centerpiece of Forward's move from static rules to model-based decisioning. This model drives tier assignment (auto-approve, conditional approve, RFI, decline) for every merchant application, replacing hand-coded thresholds with evidence-based probability scores. 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The Risk Data Scientist's job is to build the model-driven intelligence layer that replaces static rules with adaptive, evidence-based decisioning across three risk domains: merchant underwriting and approval optimization, real-time transaction fraud and anomaly detection, and AML/transaction monitoring and SAR prioritization. You will own the full modeling lifecycle - from problem framing and feature engineering to training, validation, deployment, monitoring, and regulatory governance. This is applied ML in a high-stakes, regulated financial context. Models you build will directly determine approval rates, fraud loss rates, chargeback exposure, and SAR filing quality. They will be scrutinized by bank sponsors, card networks, and regulators. 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Grobbendonk, Antwerpen, België Als Freight Forwarder ben je verantwoordelijk voor het beheer van import en exportdossiers, vanaf de boeking tot aan de facturatie. Je hebt een actieve rol in het aansturen van het logistieke traject: van het claimen van containers tot en met het magazijnbeheer en de organisatie van order picking en transport. Je fungeert als het centrale aanspreekpunt voor klanten, rederijen en interne diensten en zorgt voor een efficiënte en klantvriendelijke afhandeling. Jouw takenpakket - Volledige opvolging van dossiers: van boeking tot facturatie. - Aansturen en coördineren van het logistieke gedeelte als verlengstuk van forwarding. - Organiseren van het vervoer van de haven naar het magazijn, opvolging van lossing en stockbeheer. - Aansturen van order picking, organiseren van belading en transport. - Rapportering aan de klant. - Passieve kennis van douaneformaliteiten (transit, inklaring, fiscaal vertegenwoordiger). - Claimbeheer containers bij de rederij. - Controleren van goederen op vraag van de klant. - Trouble shooting, planning en KPI-management. Jouw profiel - Je beschikt over een opleiding of minstens een eerste ervaring als forwarder. - Je hebt ervaring met het beheer van dossiers van boeking tot facturatie. 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