🚀 Introduction

In this article, we will explore the journey of Tim Cordingly, a data scientist at Wave, an autonomous vehicle startup. Tim shares his educational background, career path, and his experiences in the world of data science. He also sheds light on the challenges he faced, as well as the unique approach that Wave is taking in the field of autonomous vehicles. Join us as we delve into Tim's story and gain insights into the exciting world of data science and autonomous vehicles.

🌍 Background

Tim Cordingly is a data scientist at Wave, an autonomous vehicle startup. Wave specializes in AV 2.0, which involves using end-to-end deep models to replace heavy sensor stacks and HD maps. They aim to create a scalable and generalizable approach to autonomous driving, with a focus on applications such as grocery delivery and ride-hailing.

🎓 Education and Career

Tim studied engineering at Durham University and did an internship with Jaguar Land Rover during his time there. After graduating, he started his career as an associate consultant at KPMG, eventually transitioning into the world of data science. He worked at Deliveroo before joining Wave, gaining valuable experience in the field and honing his skills as a data scientist.

💡 Transition into Data Science

Tim's journey into data science was not a direct one. As a child, he had a passion for building things and enjoyed subjects like physics, chemistry, and math. However, when it came to choosing a career path, he fell into engineering almost by chance. He pursued a degree in General Engineering at Durham University and specialized in mechanical engineering. Tim's interest in data science grew, and he recognized the potential of this emerging field. This led him to make a transition from KPMG to Deliveroo, where he further developed his data science skills.

🔥 Experience at Deliveroo

During his time at Deliveroo, Tim worked on various projects relating to operations and customer care. His role involved improving the efficiency of rider experiences and enhancing the customer support system. He gained valuable experience in analyzing data and making data-driven decisions to optimize different aspects of the business. Tim also had the opportunity to collaborate with operational and commercial partners to improve processes and enhance customer satisfaction.

⛈️ Challenges Faced

While working at Deliveroo, Tim faced challenges such as downtime and untested code, which could have significant impacts on the business. However, he and his team addressed these challenges quickly and efficiently. Tim emphasizes the importance of learning from such experiences and using them to improve future decision-making processes. He also highlights the importance of staying adaptable and open to new technologies and methodologies.

🌊 Joining Wave

After gaining experience in data science at Deliveroo, Tim was approached by a former colleague who was starting a data science team at Wave. Intrigued by the concept of autonomous vehicles and the innovative approach Wave was taking, Tim decided to join the company. He recognized the tremendous potential of autonomous driving as a groundbreaking technological challenge and saw it as an opportunity to make a significant impact.

🚗 Wave's Approach to Autonomous Vehicles

At Wave, Tim and his team are pioneers in AV 2.0, a radically different approach to autonomous driving. The traditional approach, AV 1.0, relies on heavy sensor stacks, HAND-engineered systems, and HD maps. In contrast, AV 2.0 leverages end-to-end deep models trained on expert driving data. This approach aims to create a scalable and generalizable solution that can adapt to different cities and vehicle platforms. Wave's technology has shown promising results in terms of generalization and scalability.

📈 Team Growth and Funding

When Tim joined Wave, the data science team was relatively small. However, with the support of substantial funding, the team has grown significantly. Wave raised a series B funding round of $200 million, allowing them to double down on their high-conviction bets and attract talented individuals who share their vision. The funding has provided them with the resources to accelerate their progress and drive innovation in the field of autonomous vehicles.

✨ Advice for Data Science Aspirants

Tim encourages aspiring data scientists to explore the different subdivisions within data science and identify their areas of interest. It is essential to gain hands-on experience by working on real-world projects and applying the tools and techniques learned. Tim emphasizes the importance of understanding problem-solving frameworks and being able to break down complex problems. Additionally, he advises aspiring data scientists to familiarize themselves with programming languages such as SQL, Python, and R, as these skills are highly valuable in the field.

In conclusion, Tim Cordingly's journey from engineering to data science and his experiences at Deliveroo and Wave highlight the exciting and ever-evolving field of data science and autonomous vehicles. Wave's innovative approach to autonomous driving has the potential to revolutionize the industry, and Tim's insights provide valuable guidance for aspiring data scientists. By embracing new technologies and having a passion for problem-solving, the possibilities in data science are limitless.

Blending Engineering and Data Science in Autonomous Vehicles