Staff Applied AI and Machine Learning Engineer, Payments & Risk

GustoGusto·Remote(Denver, CO;San Francisco, CA;New York, NY;Los Angeles, CA;Seattle, WA;Toronto, Ontario, CAN - Remote)
AI & Machine Learning
Excel

WFA Digital Insight

The demand for AI and machine learning experts in fintech has skyrocketed, with a 25% increase in job postings over the past year. Gusto's commitment to innovation and small business growth makes this role particularly compelling. As the payments and risk landscape evolves, candidates with expertise in machine learning and a passion for scalable solutions will be in high demand. Before applying, consider how your skills align with Gusto's mission and what you can bring to the table in terms of creative problem-solving and technical expertise.

Job Description

About the Role

Gusto's Data Science team is seeking a technical leader to drive machine learning and AI in the payment and risk domains. You will build a model-driven risk platform to provide a trusted environment for the Gusto Ecosystem.

Responsibilities

  • Build and deploy machine learning models to identify, assess, and mitigate risks
  • Drive research in the problem space, working with stakeholders to understand model requirements, developing models, deploying them with engineering counterparts, and monitoring performance
  • Partner with Engineering, Design, and Product counterparts in Payment and Risk to solve complex cross-functional problems
  • Develop scalable frameworks and libraries to enhance the team's core analysis and modeling capabilities

How to Stand Out

  • Familiarize yourself with machine learning frameworks and libraries, such as TensorFlow or PyTorch, to increase your chances of standing out.
  • Showcase your ability to work with cross-functional teams, including engineering, design, and product, by highlighting relevant projects in your portfolio.
  • Prepare to discuss your approach to model development, deployment, and maintenance, as well as your experience with scalability and performance optimization.
  • Be ready to provide specific examples of how you've applied machine learning to solve complex problems in the past, and how you stay up-to-date with industry trends and advancements.

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