Senior Machine Learning Modeler, Treasury

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 seeking a technical leader (individual contributor) to drive machine learning and AI in cash flow prediction. You will create innovative models that enhance the accuracy and reliability of cash flow forecasts within the Gusto Ecosystem.  

You’ll be working with an established team in Treasury Engineering, Product, Design, and Operations. In this role, you’ll collaborate across functions to build platforms that span the entire breadth of ML stacks, leveraging ML and AI to create a world-class, highly secure platform that enhances cash flow prediction accuracy, safeguards our users’ financial activities, and ensures unparalleled reliability.  You'll functionally report into Gusto Treasury as they’ll be the team setting the direction and leveraging the modeling results to optimize our cash/cash position.

Here’s what you’ll do day-to-day:

• Build and deploy machine learning models and monitoring model performance 
• 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 Treasury, Engineering, Design, and Product counterparts to solve complex cross functional problems
• Forecast daily and long term cash positions and critical variables driving balance change.  Ability to define key trends/metrics impacting balances.  Ability to leverage modeling for scenario analysis on key variable changes.
• Present and communicate results to stakeholders across the company

Here’s what we're looking for:

• 5+ years experience conducting statistical analyses on large datasets and deep domain knowledge in machine learning. 
• Proven experience in 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.  Ability to differentiate variables that are critical to the model's success.
• 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 payments, banking or Fintech industry is a plus

Our cash compensation amount for this role is targeted at $140,000-$174,000/year in Denver, Chicago, and Atlanta, $153,000-$189,000/year in Los Angeles, and $170,000-$210,000/year for San Francisco, New York and Seattle. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed above.