Data Analyst/Scientist – banking & finance

at  Adecco

If you have a Computer Science or Statistics university background and an experience in machine learning and forecasting models creation, this opportunity may be really interesting for you!

ABOUT US

Adecco is the market leader for staffing solutions in Switzerland and worldwide. Every day, our teams in our 50 locations throughout Switzerland ensure the best match between candidates and clients in various professional fields and industries. Adecco Switzerland is part of the Adecco Group, the world’s leading talent advisory and solutions company. We believe in making the future work for everyone, and every day enable more than 3.5 million careers. We skill, develop, and hire talent in 60 countries, enabling organisations to embrace the future of work. As a Fortune Global 500 company, we lead by example, creating shared value that fuels economies and builds better societies. Our culture of inclusivity, entrepreneurship and teamwork empowers our 30,000 employees. We are proud to have been consistently ranked one of the ‘World’s Best Workplaces’ by Great Place to Work®. The Adecco Group AG is headquartered in Zurich, Switzerland (ISIN: CH0012138605) and listed on the SIX Swiss Exchange (ADEN). The Group is powered by three global business units: Adecco, Talent Solutions and Modis

Objective:

Contributing to the development of our cutting-edge authorization and fraud prevention systems, preventing fraud losses, optimizing financial authorization rules as well as keeping authorization rules conformed with the mandates of the circuits

• Developing advanced algorithms: Utilize machine learning, statistical modeling, and data mining techniques to develop predictive models for fraud detection and prevention
• Data Analysis: Analyze large volumes of transactional data to identify suspicious patterns, trends, and anomalies useful to detect frauds as well as optimizing authorization rules
• Feature Engineering: Engineer relevant features from diverse datasets to enhance the performance of fraud detection models
• Model Evaluation and Optimization: Continuously evaluate the effectiveness of fraud detection models, fine-tuning parameters and algorithms to improve detection accuracy while minimizing false positives
• Reporting: Developing dashboards to monitor the business performance of our authorization and fraud prevention systems
• Collaboration: Collaborate with cross-functional teams including IT and business stakeholders to implement and integrate new features into our existing systems.
• Card schema regulations: Follow and implement best practices and regulatory requirements to assure the compliance of our authorization rules