Employer: Gusto

About the Role

Gusto is looking for an ambitious Data Scientist with a solid grounding in statistics and credit risk and at least a few years experience applying this knowledge in a business environment. In this role you will work closely with other members of our Data Science team, as well as our Engineering, Product, Design, and Risk Operations teams to define and track product metrics, contribute to credit underwriting tools and models, design customer-facing experiments and dive deep into our Payroll, Benefits and HR data to deliver insights. Gusto’s Data Science team operates full-stack, working closely with product managers to apply data insights to strategy and product decisions, conducting analyses, prototyping and deploying predictive models and statistical tools both for internal use and for our customers.

Here’s what you’ll do day-to-day:

  • Work closely with product groups to define, measure and report on core product and feature metrics, and to define standards and practices for how product groups work with data
  • Build and deploy models and data products to support growth and control risk within our segment
  • Perform in-depth analyses of our Payroll, Benefits and HR product data to inform and guide product direction and strategy
  • Design and analyze customer-facing experiments
  • Work with our business intelligence teams to turn your insights and analyses into clean and consistent reporting
  • Be a strong voice for a data-informed point of view within our engineering, product and design organization
  • Collaborate with UX research to design surveys and provide quantitative insights on customer experience
  • Enhance and contribute to the team’s core analysis and modeling systems and libraries
  • Identify new opportunities to leverage data to improve Gusto’s products and help our business
  • Present and communicate results to stakeholders across the company

Here’s what we’re looking for:

  • At least 5-10 years experience conducting statistical analyses on large datasets, ideally in financial software or finance industries. Specific experience with credit risk modeling is a plus.
  • 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 – generalized linear modeling, regularization, ensemble models (e.g., random forest, gradient boosting), Bayesian analysis methods
  • Strong programming skills in SQL – comfortable with all phases of the data science development process, from initial analysis and model development all the way through to deployment
  • Strong knowledge of statistics and experiment design and ability to apply these to conducting and interpreting analyses, data visualization, presentation and recommendations
  • Excellent communication skills – able to effectively deliver findings and recommendations to non-technical stakeholders in a clear and compelling fashion
  • Passionate about teaching and evangelizing a data-informed approach to product development to product managers, designers and engineers
  • PhD or Masters plus equivalent experience in a quantitative field