Sr. Network Planning Manager , EU SSD-Direct

Amazon UK Services Ltd.
London
1 year ago
Applications closed

Related Jobs

View all jobs

Sr. Project Manager/Program Manager - Digital Twin / AIOps (OSS)

Sr Product Manager, Data Science

Sr. Data Scientist

Senior Data Scientist

Are you excited about solving operational challenges, and want real responsibility and ownership for how your program performs? The Sub Same Day team in AMZL Org is looking for a Principal Planner with a proven record to own part of the delivery program for an exciting new customer offering with faster delivery times.

Please note that this role can be located in London or Luxembourg.

SSD-Direct is at the forefront of last mile and ultra-fast fulfillment/delivery innovation. As Principal Planner on the Delivery team, you’ll have the opportunity to sit at the apex where technology meets operations. We’re looking for a leader capable of diving deep into the operation, formulating key insights, and defining and driving projects to improve our network on behalf of our customers. You will be a key member of the team responsible for collaborating with many partner teams from operations to tech/engineering to find opportunities, and build short term and long term roadmaps to address. The ideal candidate for this role will be highly analytical, creative, motivated, have strong business judgment and a talent for problem solving in a high paced and ambiguous environment.

Responsibilities include:
- Ownership of E2E delivery performance focusing on quality and cost
- Strategic workstream to improve Same Day offering across EU and UK
- Working on out-of-the-box initiatives to holistically improve Speed in EU and UK.
- Responsibility for apprising multiple senior stakeholders (L10+) in the different forums about the program and latest updates.
- Leading Monthly reporting on KPIs and projects in the glidepath.

A successful candidate will demonstrate key skills which will be required:
- Strong analytical and quantitative skills to identify opportunities, estimate improvements, streamline and automate their work.
- Knowledge of operations, continuous improvement and structured problem solving (which may include Lean Six Sigma or Theory of Constraints).
- Ability to earn trust and influence a broad group of stakeholders worldwide to achieve program goals.
- Curiosity and ability to understand complex technical systems and processes.
- Forensic attention to detail.
- Ability to manage multiple, competing priorities simultaneously.
- Ability to work in a nascent, rapidly changing and ambiguous environment.
- Ability to clearly communicate in both written and spoken form to stakeholders at different seniority and in different functions.
- A willingness to roll up your sleeves and do whatever is necessary; the mentality of an owner.


BASIC QUALIFICATIONS

- Prior experience working cross functionally with tech and non-tech teams experience
- Prior program or project management experience
- prior experience of managing, analyzing and communicating results to senior leadership experience
- Bachelor's degree in Supply Chain, Data Science, or Engineering
- Experience implementing repeatable processes and driving automation or standardization
- Experience defining program requirements and using data and metrics to determine improvements


PREFERRED QUALIFICATIONS

PREFERRED QUALIFICATIONS
- Experience delivering projects within scope, time, budget and quality

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.