Experis® is a global leader in IT professional resourcing, permanent recruitment, project solutions, and managed services. With over 25 years of experience in Switzerland and offices in Zürich, Basel, and Geneva, we connect top IT talent with leading companies. Our mission is to help professionals unlock their potential and thrive in dynamic, future-ready careers. Location: Zurich Start date: ASAP Duration: until 31.12.2026- with possibility of extension Responsibilities Refactor the existing categorical (Low/Medium/High) risk assignment process into a continuous or tiered numeric scoring system. Develop a points-based scoring engine inspired by best practices in credit risk and fraud detection, where each KYC attribute contributes a defined number of points according to its relative risk signal. Apply interpretable machine learning and statistical techniques suitable for regulated environments (e.g., logistic regression with WOE, or tree-based methods). Define and calibrate scoring bands, ensuring clear thresholds and justifiable breakpoints. Build reproducible Python pipelines for scoring calculation, backtesting, and sensitivity analysis, ensuring seamless handover to downstream teams. Own the full model lifecycle: business requirements gathering, feature engineering, development, validation, deployment, monitoring, documentation, and maintenance. Qualifications Minimum 7 years of professional experience in quantitative roles applying advanced data science and statistical modelling within top-tier banks, asset managers, or fintech firms specializing in capital markets. Master's degree or PhD in Quantitative Finance, Mathematics, Physics, Engineering, or a closely related field; advanced academic training combined with real-world application in financial markets is highly valued. Master-level command of Python for data science, including object-oriented programming, modular architecture, exception handling, performance profiling, and test-driven development. Deep hands-on experience with pandas, NumPy, scipy, scikit-learn, statsmodels, and either PyTorch or TensorFlow; proficiency in building reusable packages and internal libraries. Strong proficiency in SQL, with extensive experience writing optimized queries over large-scale datasets, particularly in Oracle. Exceptional analytical rigor, demonstrated by past work that challenged flawed assumptions, improved model robustness, or prevented false conclusions from spurious correlations, look-ahead bias, or poor experimental design. Fluent inEnglish (spoken and written). Due to Swiss work permit restrictions, we can only consider applications from EU citizens or holders of valid working permit in Switzerland. #J-18808-Ljbffr
Senior Data Scientist - 80% Arbeitgeber: Experis Schweiz
Als Arbeitgeber bietet unsere traditionsreiche, unabhängige Schweizer Privatbank nicht nur ein dynamisches und herausforderndes Arbeitsumfeld im Bereich Trading, sondern auch zahlreiche Möglichkeiten zur beruflichen Weiterentwicklung. Mit einem starken Fokus auf Teamarbeit und Innovation fördern wir eine Kultur, die Kreativität und Eigenverantwortung schätzt, während wir gleichzeitig unseren Mitarbeitern ein attraktives Paket an Vorteilen und eine ausgewogene Work-Life-Balance bieten. Arbeiten Sie in einem sicheren und komplexen Umfeld, das Ihnen die Möglichkeit gibt, Ihre Fähigkeiten in der Entwicklung von hochmodernen Finanzanwendungen voll auszuschöpfen.
