Staff Applied Modeler, Risk

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. 

Here’s what you’ll do day-to-day:

• 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
• 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

Here’s what we're looking for:

• 8+ years experience conducting statistical analyses on large datasets and deep domain knowledge in machine learning. 
• Proven experience in the credit risk modeling or fraud risk modeling using logistic regression, random forest, Xgboost or neural networks
• 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).
• Strong programming skills - comfortable with all phases of the data science development process, from initial analysis and model development all the way through to deployment
• Excellent communication skills - able to effectively deliver findings and recommendations to non-technical stakeholders in a clear and compelling fashion
• PhD or Masters plus equivalent experience in a quantitative field is a plus
• Experience in the Fintech industry is a plus

Our cash compensation amount for this role is targeted at $170,000 - $205,000 in Denver, $185,000- $225,000 in Los Angeles,  $205,000 - $250,000  for San Francisco, New York, and Seattle, and $185,000 - $225,000 CAD for Toronto, Canada. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed above.