Top Fintech Data Science Jobs

The Senior Data Scientist will lead AI and machine learning projects, mentor junior staff, and develop innovative algorithms for data governance and new product initiatives.

The Director of Data Science will build analytic models, support decision making with data insights, and create visualizations from structured and unstructured data.

The Senior Data Scientist will develop and deploy machine learning models for fraud detection, working with cross-functional teams to improve risk scoring and mitigate fraud risks.

The Lead Data Scientist will develop data capabilities, analyze data for insights, and create machine learning solutions to enhance Visa's offerings, accomplishing key contributions to the company's data strategy.

The Staff Data Scientist will develop machine learning models, collaborate with cross-functional teams, and use analytics to drive business growth and improve credit risk assessment.

As an AI-First Data Scientist, you will design and deploy ML solutions, conduct causal analysis, and automate intelligence in decision-making processes. You will work closely with senior operators and business leaders, translating complex problems into data-driven solutions while ensuring model performance and scalability.

The Data Scientist will analyze customer data, build performance metrics, drive product improvements, and partner with diverse teams to ensure reliable fraud detection systems.

The Data Scientist II will analyze large datasets, develop predictive models, evaluate business performance, and interact with stakeholders to provide insights.

The Senior Manager of Credit Risk Analytics oversees credit strategies and risk management for Personal Loan portfolios, ensuring compliance, optimizing performance using ML, and collaborating cross-functionally to enhance business growth and minimize credit losses.

The VP, Data Analytics will lead the Business Intelligence team by managing analytic solutions, data automation, and reporting. This role requires collaboration with stakeholders to gather requirements, develop a data warehouse, and mentor a team of analysts, focusing on process improvement and efficient solutions.