Machine Learning Engineer, Payments & Risk

To apply for the Machine Learning Engineer position at Gusto, candidates should ideally have 8+ years of experience in statistical analysis on large datasets. A master's or PhD in a quantitative field is preferred, with a strong foundation in machine learning techniques using languages such as Python or R. Experience in the Fintech or risk management domains is a plus.

As a Machine Learning Engineer at Gusto, you will build and deploy machine learning models to identify and mitigate risks associated with payments. You'll research problem spaces, collaborate with various teams, develop scalable frameworks, and present results clearly to stakeholders to improve Gusto's product offerings.

Gusto fosters a collaborative work environment by encouraging cross-functional teamwork. As a Machine Learning Engineer, you will engage with experts from Engineering, Product, and Design to solve complex challenges together, ensuring your contributions effectively enhance our products.

Machine Learning Engineers at Gusto typically use programming languages like Python and R for statistical modeling and machine learning tasks. Familiarity with other techniques such as predictive modeling, anomaly detection, and natural language processing may also be beneficial.

Yes, the Machine Learning Engineer position at Gusto can be performed remotely, especially for candidates located in Toronto, Ontario. However, employees may need to work from one of Gusto's physical offices in New York, San Francisco, or Denver on certain designated days.

In the interview process for a Machine Learning Engineer role at Gusto, you can expect to discuss your technical expertise, problem-solving abilities, and past experiences with data science and machine learning. Prepare for both technical questions and scenarios assessing communication skills.

Gusto offers a dynamic environment with numerous opportunities for career advancement for Machine Learning Engineers. As you contribute to critical projects and enhance your skills, you can explore roles in leadership or specialized areas within data science and machine learning.

When answering this question, focus on specific projects where you built and deployed machine learning models. Highlight the frameworks you used, the challenges you faced, how you ensured the model's performance over time, and any collaborative efforts with other teams to enhance the deployment process.

In your answer, mention the statistical techniques you commonly apply, such as regression analysis, clustering, or anomaly detection. Discuss their relevance to the tasks you typically perform and how they contribute to informed decision-making within finance and risk management.

Emphasize your approach to simplifying complex data insights and findings for non-technical stakeholders. Discuss the importance of tailoring your message, using visuals or summaries, and ensuring that everyone understands the implications of your work on Gusto’s objectives.

Choose a specific example of a challenging data problem in your experience. Outline the steps you took to analyze the data, the modeling techniques you employed, how you collaborated with colleagues, and the successful outcome of your efforts. Be ready to explain your reasoning and decisions throughout the process.

Explain your process for validating machine learning models, covering aspects like using a separate validation dataset, cross-validation techniques, tracking metrics, and employing feedback loops to improve model accuracy. Make sure to highlight any tools or frameworks you've used in this context.

Discuss your methods for managing multiple projects, such as assessing project impact, setting priorities based on deadlines and resource availability, and communicating effectively with your team. Provide examples of how you've successfully juggled competing demands in the past.

Share a specific instance where your data analysis led to a significant business decision. Detail the problem, the type of data you analyzed, the models employed, and the measurable outcomes stemming from the implementation of your findings.

Discuss your preferred tools for data analysis, such as Python libraries like Pandas or Scikit-learn, R, or visualization tools like Tableau. Explain why you've chosen these tools based on factors like ease of use, effectiveness, or past success in streamlining your workflow.

Highlight your commitment to continuous learning by mentioning blogs, courses, or conferences you attend. Discuss any online communities you engage with, as well as how you apply new knowledge or techniques in your work to keep pace with industry trends.

Articulate your thoughts on the evolving landscape of Payments and Risk. Discuss the potential for machine learning to enhance fraud detection, customer personalization, and risk assessment, emphasizing how your experiences make you well-equipped to contribute to these advancements at Gusto.