We are seeking a highly skilled and experienced Senior / Staff Data Scientist to join our Planner Verification & Validation team and play a pivotal role in developing and deploying cutting-edge autonomous driving technologies. As a Senior / Staff Data Scientist, you will be responsible for tackling complex data challenges, driving innovation in machine learning and statistical modeling, and contributing to the advancement of our self-driving systems. You will work closely with a team of talented engineers and researchers in a fast-paced, collaborative environment.
In this role, you will:
- Analyze large-scale datasets from various sources, such as fleet and simulation data, to extract actionable insights.
- Develop and evaluate novel algorithms and methodologies for data processing, feature engineering, model training, and performance optimization.
- Evaluate and validate model performance through rigorous testing and analysis, ensuring the safety and reliability of our systems.
- Communicate complex technical findings and recommendations to both technical and non-technical audiences.
- Mentor and provide technical guidance to junior data scientists and engineers.
Qualifications
- Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field with 5+ years of experience as a Data Scientist.
- Strong programming skills in Python, including experience with relevant libraries such as scikit-learn, and Pandas & SQL.
- Expertise in a wide range of machine learning techniques, including deep learning, reinforcement learning, and statistical modeling.
- Experience working with large-scale datasets and distributed computing platforms (e.g., Spark, Hadoop) with solid understanding of data processing, feature engineering, and data visualization techniques.
- Strong communication and presentation skills, with the ability to effectively convey technical information to diverse audiences.
Bonus Qualifications
- Experience working with autonomous driving data and algorithms, robotics, control systems or related fields.
- Experience with real-time data processing and low-latency systems.
- Publications in top-tier machine learning or autonomous driving conferences.
$217,000 - $295,000 a year
Base Salary Range
There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate's relevant years of experience, domain knowledge, and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position.
Zoox also offers a comprehensive package of benefits, including paid time off (e.g. sick leave, vacation, bereavement), unpaid time off, Zoox Stock Appreciation Rights, Amazon RSUs, health insurance, long-term care insurance, long-term and short-term disability insurance, and life insurance.
About Zoox
Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team.
Accommodations
If you need an accommodation to participate in the application or interview process please reach out to [email protected] or your assigned recruiter.
A Final Note:
You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.
Tags & focus areas
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