At a Glance
- Tasks: Build machine learning models for Amazon's transportation planning systems and optimise operations.
- Company: Join Amazon's innovative Middle Mile Science group, a leader in freight transportation.
- Benefits: Competitive salary, diverse work culture, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on diversity and inclusion.
- Why this job: Make a real impact on logistics with cutting-edge technology and innovative solutions.
- Qualifications: PhD or Master's with experience in programming and optimisation algorithms.
The predicted salary is between 60000 - 80000 £ per year.
Amazon's Middle Mile Science group is looking for an Applied Scientist to build machine learning and optimization models for large-scale transportation planning systems. This includes the development of dynamic pricing and network planning models to improve operations and services for our external freight customers.
The Middle Mile Science group develops optimization and machine learning systems that power Amazon's freight transportation network, from network design and pricing to real-time load planning and capacity utilization. The scale of Amazon's fulfillment operations requires robust transportation networks that minimize cost while meeting all customer deadlines. Real-time execution depends on state-of-the-art optimization and artificial intelligence to coordinate thousands of operators and drivers. This includes shipper-facing and carrier-facing marketplace algorithms as well as network planning and optimization tools. Amazon often finds that existing techniques do not match our unique business needs, driving the innovation of new approaches and algorithms.
As an Applied Scientist focusing on external freight within middle mile transportation, you will work closely with business leaders, engineers, and fellow scientists to design and build scalable products operating across multiple transportation modes. You will create experiments and prototypes of new machine learning and optimization applications, present research findings to senior leadership, and implement your models within production systems. You will write production-quality code designed for scalability and maintainability, and make decisions that affect how we build and integrate algorithms across our product portfolio.
About The Team: Our Middle Mile Marketplace Science team builds the algorithms for Amazon’s rapidly growing freight marketplace. Amazon contracts with 3P shippers and a network of independent carriers, using a mix of contract structures with varying service and risk profiles. Our work focuses on mechanisms and learning algorithms to optimize pricing and matching in this complex marketplace, and continually improve the experience for carriers and shippers. This is an area with many challenging problems and a huge business impact for Amazon.
Basic Qualifications:
- Experience programming in Java, C++, Python or related language
- Experience with discrete and continuous optimization methodologies and algorithms
- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics, or equivalent quantitative field, or Master’s degree and 5+ years of industry or academic research experience
- 3+ years of building machine learning or optimization algorithms for business applications
Preferred Qualifications:
- Experience in professional software development
- Experience in standard machine-learning and statistical modeling tools and techniques (e.g. random forests, gradient-boosted regression, LASSO, logistic regression)
- Strong track record of publications at top-tier journals or conferences
- Experience with integer programming, dynamic programming, and/or stochastic optimization
Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build.
Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice to know more about how we collect, use and transfer the personal data of our candidates.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit our website for more information.
Applied Scientist, Amazon Transportation in London employer: Amazon Science
Amazon is an exceptional employer, offering a dynamic work environment where innovation thrives. As an Applied Scientist in the Middle Mile Science group, you will collaborate with talented professionals to develop cutting-edge machine learning and optimization models that directly impact our freight transportation network. With a strong commitment to employee growth, diversity, and a culture that values creativity and collaboration, Amazon provides unique opportunities for meaningful contributions in a fast-paced, technology-driven setting.
StudySmarter Expert Advice🤫
We think this is how you could land Applied Scientist, Amazon Transportation in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at Amazon. LinkedIn is your best mate here—connect, engage, and don’t be shy to ask for informational chats. You never know who might help you land that interview!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning and optimisation projects. Whether it’s GitHub repos or a personal website, let your work speak for itself. This is your chance to shine and demonstrate what you can bring to the table.
✨Tip Number 3
Prepare for the technical interview like it’s the final exam! Brush up on your coding skills and optimisation algorithms. Practice common interview questions and maybe even do mock interviews with friends or mentors. Confidence is key!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, keep an eye on job alerts so you can jump on new opportunities as they pop up. We’re rooting for you!
We think you need these skills to ace Applied Scientist, Amazon Transportation in London
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter to highlight your experience with machine learning and optimisation. We want to see how your skills align with the specific needs of the Applied Scientist role, so don’t hold back on showcasing relevant projects!
Show Off Your Coding Skills:Since programming is key for this position, include examples of your coding experience in Java, C++, or Python. We love seeing production-quality code, so if you’ve got any projects or contributions to share, make sure to mention them!
Highlight Your Research Experience:If you've published papers or presented at conferences, let us know! A strong track record in research can really set you apart, especially in areas like optimisation methodologies and machine learning techniques.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re serious about joining our team at Amazon!
How to prepare for a job interview at Amazon Science
✨Know Your Algorithms
Make sure you brush up on your knowledge of machine learning and optimisation algorithms. Be ready to discuss specific techniques you've used, like random forests or dynamic programming, and how they can be applied to real-world problems in transportation.
✨Showcase Your Coding Skills
Since you'll be writing production-quality code, it's crucial to demonstrate your programming skills during the interview. Prepare to solve coding challenges in languages like Python or Java, and explain your thought process as you go.
✨Prepare for Problem-Solving Questions
Expect to face questions that test your problem-solving abilities. Think about complex scenarios related to transportation planning and how you would approach them using optimisation models. Practice articulating your thought process clearly.
✨Engage with the Team's Vision
Familiarise yourself with Amazon's Middle Mile Science group and their goals. Be prepared to discuss how your background aligns with their mission to improve freight operations and services. Showing genuine interest in their work can set you apart.
