AI Applied Scientist (Machine Learning) - MoneyLion

You will use data, machine learning, and AI to solve complex problems and create meaningful impact in a hands-on role that combines experimentation, solution development, and technical innovation. You'll leverage a range of techniques to develop effective solutions to complex problems, explore emerging technologies and research, and transform ideas into meaningful capabilities that deliver practical impact. You'll enjoy building, learning, and experimenting with new approaches while maintaining a strong focus on delivering meaningful outcomes.

Responsibilities

• Solve complex problems through data, experimentation, and evidence-based approaches
• Design, build, and improve intelligent solutions and capabilities
• Build and evolve solutions from concept to production
• Collaborate with scientists, engineers, and product teams to deliver impactful outcomes
• Contribute to responsible AI practices, knowledge sharing, and the adoption of effective AI solutions
• Stay current with emerging technologies, research, and industry developments, and apply relevant innovations where appropriate
• Contribute to high-quality solutions through experimentation, evaluation, and continuous improvement

Requirements

• Curious, analytical, and passionate about solving problems using data, machine learning, AI, and emerging technologies
• Hands-on experience building data-driven applications, analytical solutions, intelligent systems, or AI-powered capabilities
• Familiarity with machine learning, statistical, analytical, or AI techniques
• Strong technical judgment, software development, and problem-solving skills
• Able to independently drive projects from idea to implementation
• Able to evaluate different approaches and make practical technical decisions
• Strong communication skills and ability to collaborate effectively with diverse teams
• Experience developing data-driven applications, algorithms, predictive models, optimization solutions, or similar intelligent capabilities
• Experience working with complex systems, datasets, or production environments
• Contributions to open-source projects, technical communities, hackathons, research initiatives, publications, or personal projects