On our Data Science team we work closely with Product, Growth, & Engineering to optimize our marketplace, impact growth strategy, & create valuable tooling. Every decision at Ritual relies on well thought out, evidence-based analysis, making data one of our most valuable strategic resources. Our data scientists leverage methods from statistics, computer science, & other quantitative disciplines to understand marketplace behaviour & extract meaningful patterns from our large data sets.
This hire will be an important addition to our expanding team, & will help lead the development of our product experiments analysis framework, as well as becoming our go-to expert on how to apply causal inference methods to our everyday analysis. Our product development teams are constantly experimenting with product changes, meaning that optimizing this process to be as rigorous & efficient as possible will have a profound impact on the success of our organization.
Outside of pushing our product experiment analysis forward, this role will also have the opportunity to generate important insights to inform our growth marketing & product development strategies. This means working directly with stakeholders to identify the most impactful questions at hand & then using your preferred toolset to find answers in our data. This role requires a self-directed, experienced candidate with a demonstrated command of applied statistics, causal inference, SQL, scientific programming, & a passion for being collaborative & solving complex quantitative problems. Please note that this role will not necessarily be focused on applications of machine learning unless the task at hand requires it.
You will also have the opportunity to help mentor teammates & push to add more statistical rigour throughout all our processes & analyses. Our team members come from a widely diverse set of backgrounds, with high variance in their given strengths. Were excited to have the opportunity to add to this diverse & fun team.
- BA/BS degree in Statistics, Econometrics, Epidemiology, or a related quantitative field, or equivalent practical experience.
- Experience in a programming language commonly used for data manipulation & computational statistics (such as Python, R, Julia, Matlab, Stata), & with SQL.
- Demonstrated expertise in multivariate statistics, hypothesis testing, & the design of experiments.
- Practical experience deploying causal inference methods for estimation of treatment effects in observational data. Plus you like to draw causal DAGs.
- MS or PhD in Statistics, Econometrics, Epidemiology, or a related field.
- Proficiency in programming computational & statistical algorithms for large data sets.
- Experience with geospatial data analysis is a plus.
- Experience in a role analyzing business, growth marketing or product development data.