Staff Applied AI and Machine Learning Engineer, Payments & Risk
About The Position
At Gusto, we're on a mission to grow the small business economy. We handle the hard stuff—like payroll, health insurance, 401(k)s, and HR—so owners can focus on their craft and customers. With teams in Denver, San Francisco, and New York, we’re proud to support more than 400,000 small businesses across the country, and we’re building a workplace that represents and celebrates the customers we serve. Learn more about our Total Rewards philosophy . About the Role: Gusto’s Data Science team leverages Gusto’s rich dataset to guide product direction and decision-making. We operate full-stack, conducting analyses, prototyping and deploying predictive models and statistical tools both for internal use and for our customers. For this role, we are looking for a technical leader (an individual contributor) 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 Gusto Ecosystem. You’ll be working with an established team and seasoned payments and risk leaders in Engineering, Product, Design, Operation, Identity and Compliance. In this role, you’ll work cross functionally to build Platforms that span the entire breadth of the Payments and Risk Stacks, and use ML and AI to build a world- class, high secure platform that safeguards our users’ activities and money, and ensures unparalleled reliability.
Requirements
- 8+ years of experience conducting statistical analyses on large datasets and deep domain knowledge in machine learning and artificial intelligence, including familiarity with Large Language Models (LLMs) and their applications. This could mean either a MS or PhD in a quantitative field with at least 5 years experience in a business environment, or BS or Data Science Bootcamp graduate with at least 8 years of experience working as a data scientist or a machine learning engineer in a business setting.
- Proven experience in credit risk modeling or fraud risk modeling using logistic regression, random forest, Xgboost or neural networks, along with a strong understanding of AI-based approaches and the potential of LLMs to enhance traditional models.
- Experience applying a variety of statistical and modeling techniques using Python, R or another statistical modeling language, as indicated by familiarity with many of the following techniques - predictive modeling, anomaly detection, ensemble methods, natural language processing (NLP, optional).
- Basic understanding of LLMs and their applications.
- Strong programming skills - comfortable with all phases of the data science development process, from initial analysis and model development to deployment
- Excellent communication skills - able to effectively deliver findings and recommendations to non-technical stakeholders in a clear and compelling fashion
Nice To Haves
- PhD or Masters plus equivalent experience in a quantitative field is a plus
- Experience in the Fintech industry is a plus
Responsibilities
- Build and deploy machine learning models to identify, assess and mitigate risks
- Responsible for driving research in the problem space, working with stakeholders to understand model requirements, developing the model from scratch, deploying the model alongside your engineering counterparts, and monitoring and maintaining the model’s performance over time
- Partner with Engineering, Design, and Product counterparts in Payment and Risk to solve complex cross functional problems
- Develop scalable frameworks and libraries that enhance and contribute to the team’s core analysis and modeling capabilities, including through the integration of LLMs to improve data processing, analysis, and insights.
- Identify new opportunities to leverage data to improve Gusto’s products and help risk management team to understand business requirements and develop tailored solutions
- Present and communicate results to stakeholders across the company
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Number of Employees
1,001-5,000 employees
