Data Scientist, Data Quality & Provenance
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What you'll do
- 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
What they're looking for
- 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
- You are deeply curious about building something new and relish the idea of helping to define AV2.0 and how we build it.
Nice to have
- 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.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
The Role
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.
This means you might:
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
About you
Essential:
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
You are deeply curious about building something new and relish the idea of helping to define AV2.0 and how we build it.
Desirable:
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.
This role is a full-time role based in Stuttgart, Germany. At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home. We operate core working hours so you can determine the schedule that works best for you and your team.
Company
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