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Data Scientist

Job in Sunnyvale, Santa Clara County, California, 94087, USA
Listing for: Icehouseventures
Full Time position
Listed on 2026-09-18
Job specializations:
  • IT/Tech
    Data Scientist, Machine Learning/ ML Engineer, AI Engineer (Applied/Software), Data Analyst
Salary/Wage Range or Industry Benchmark: 100000 - 130000 USD Yearly USD 100000.00 130000.00 YEAR
Job Description & How to Apply Below

At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law.

About us

Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.

Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.

In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future.

Job Overview

As a Data Scientist in the Experimentation and Product group, you will focus on generating robust, actionable insight that helps our product, data and research teams prioritise their efforts today and identify opportunities for tomorrow.

Rather than focusing on using black-box models to optimize ML performance, DS focus on using experimental and causal inference methods to generate statistically-robust and highly interpretable findings. This is a fascinating opportunity to pioneer data science methodologies in a very complex and novel space in AV.

Responsibilities

This means you might:

  • Build the frameworks that best synthesise our complex video and simulation data, and use it to facilitate analytics-driven strategy from individual product teams all the way to the company level
  • Formulate and iterate upon the performance metrics Wayve should focus on, to best measure success in an AV 2.0 world
  • Develop and deploy novel experimental techniques to improve signal-to-noise ratio and reduce time to feedback
  • Combine experiment methods with offline causal inference techniques
What we are looking for in our candidate Essential
  • 3+ years experience working in a Data Science role.
  • Comfortable 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, stats models, 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.
  • You are deeply curious about building something new and relish the idea of helping to define AV2.0 and how we build it.
Desired
  • 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.…
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