About the job
As a Data Scientist supporting AI engineers, you will partner with one or more engineering teams, developing actionable insights that guide improvements to the Wayve AI Driver. Using experimental and observational analyses of real and simulated driving, you will help teams advance the functionality, safety, and performance of the Wayve AI Driver, helping to advance Wayve as the leader in end-to-end AI for autonomous mobility.
Responsibilities
Formulate and iterate upon the performance metrics that organize our engineering efforts and guide progress toward commercial success
Design experiments and targeted off-road measurements to ensure that we deliver product requirements to customers while maintaining safety and performance
Investigate factors in model training and inference leading to bottlenecks in functionality and performance, identifying and validating hypotheses for unlocking improvements
Qualifications
Minimum
3+ years experience working in a Data Science role
Fluent in querying and building large datasets, writing production-level SQL for use in data-transformation pipelines
Prior experience designing robust real-world experiments (e.g. A/B) and critically evaluating test-statistics
Foundations in the fundamentals behind statistics: testing appropriate distributions, testing the assumptions behind frequentist stats
Proficient in using a statistical scripting language and data science/ML packages (e.g. python such as pandas, sklearn, statsmodels, scipy or R such as dplyr, caret, stats)
Well-versed in summarising, visualising and communicating findings in an accessible and compelling way
Track record of influencing team direction through your findings
A bias towards deriving actionable insight that can be used to drive prioritisation and strategy for others
Comfortable working asynchronously across time zones with cross-functional partners
Preferred
Practical experience with machine learning (e.g. PyTorch)
Passion to take research ideas to production
Track record of promoting statistical rigour and experimental best practices in your prior roles
Prior experience using causal inference/econometric techniques and bayesian methodologies for hypothesis testing
Prior experience using large datasets with distributed computing (e.g. spark, hadoop or other map-reduce tech)
Experience working in a fast-moving tech company or startup