Top Fintech Data Science Jobs

As a Data Scientist, you'll develop machine learning models for credit and fraud risk management, optimize underwriting decisions, and improve risk strategies using complex data analysis.

As a Staff Data Scientist II, you'll develop AI-driven monetization strategies and lead the design of scalable machine learning systems, collaborating across teams to innovate ad technology.

The Senior Data Scientist leads data analysis and model development projects, leveraging machine learning to drive insights and solutions. Responsibilities include executing data strategies, developing predictive models, collaborating with teams, ensuring model validity, and guiding junior members in a dynamic environment.

The AI Data Analyst will engineer data extraction pipelines, conduct statistical analysis, validate metrics with AI, and support organizational insights through rigorous analysis.

The Data Scientist at Candescent uses statistical and AI/ML methods to analyze data, support product strategy, and drive data-informed decision-making. Responsibilities include user behavior analysis, product experimentation, and risk assessment through advanced analytics, while collaborating with cross-functional teams.

The Senior Data Scientist will leverage advanced analytics and machine learning to enhance pricing and underwriting processes, collaborating with various functional teams to develop and implement predictive models and analytics solutions.

The Data Science & Gen AI Technical Group Manager leads AI initiatives, manages a team of data scientists, and ensures the delivery of innovative AI solutions for the organization.

The Data Scientist/Machine Learning Engineer will enhance product listing quality using ML/AI, working cross-functionally to implement high-performance solutions.

As a Pricing Data Scientist, you'll develop and validate pricing models for alternative assets, collaborate with engineers, and enhance valuation accuracy through data insights.

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.