How to get a job at Amazon: Yashar Ahmadov's experience

Yashar Ahmadov, City — Luxembourg, Senior Data Scientist at Amazon, LinkedIn

About Me

I am a supply chain and modeling specialist with an educational background in industrial engineering, operations management, and data analytics. Over the last decade, I have worked in manufacturing, logistics, and the design of large-scale networks, focusing on one key question: how do complex systems behave under uncertainty, and how can we make better decisions before implementing them in the real world?

My work sits at the intersection of modeling, optimization, and analytics. Instead of optimizing a single KPI, I study trade-offs at the system level—cost versus speed, inventory versus service, resilience versus efficiency. This approach has taken me from operational roles in manufacturing and planning to developing and deploying models that influence billion-dollar decisions in global supply chains.

Alongside my corporate work, I maintain close ties with academia and applied research. My articles have been published in peer-reviewed journals, I have taught courses on operations and modeling, and I work on transforming complex analytical insights into understandable stories for decision-making.

Currently, I work at Amazon in Luxembourg as a Senior Data Scientist. I joined Amazon in late 2019 and have held several roles within the company, starting as a Business Analyst, then as a Data Modeling Specialist, and now in a senior position.

In my role, I engage in large-scale modeling and optimization of order fulfillment processes and transportation networks. These models help evaluate strategic and tactical decisions under uncertainty, often influencing network design, capacity planning, and operational policies across different regions.

How to Get into Amazon

I joined Amazon after several years of working in manufacturing and project roles in different countries. At that time, I wanted to get closer to system-level decision-making, where analytics and modeling directly shape strategy rather than just supporting task execution.

Amazon attracted me for two reasons. First, the company manages supply chains at a scale where small improvements have a massive impact. Second, it has a culture that encourages deep problem-solving, ownership, and a willingness to challenge existing assumptions with data.

The hiring process was rigorous and structured. It included several stages: first, screening interviews focused on analytical thinking and problem-solving, followed by technical interviews where I had to reason through modeling and data-driven scenarios.

The final stage was a panel interview combining technical depth with behavioral questions based on Amazon’s Leadership Principles. I was particularly impressed that the interviewers evaluated not memorized answers, but how you think, how you structure uncertainty, and how you learn from past decisions.

Advice for Those Who Want to Work in an International Company

1. Develop depth before breadth. International companies value specialists who deeply understand a problem, not just those with superficial knowledge.
2. Learn to explain complex concepts in simple terms. In a global environment, you work with people from different countries, cultures, and areas of expertise. The ability to clearly explain a complex idea is often more important than the idea itself.
3. Interviews are not about perfect answers. It is important to demonstrate structured thinking, intellectual honesty, and the ability to learn. If you do not know something, explain how you would approach solving it—this approach is valued higher than a correct answer.

Work Specifics

My current work focuses on creating and maintaining end-to-end optimization and analysis models that evaluate the flow of packages through Amazon’s order fulfillment and transportation networks. These models account for uncertainty in demand, processing times, routing, and capacity constraints.

My role is not limited to modeling. I define problems, translate business questions into mathematical models, ensure models are production-ready, and collaborate closely with stakeholders to correctly interpret results. A key part of the job is making insights useful for decision-making, not just technically impressive.

I use Java and Python for modeling and optimization logic, as well as AWS services such as EC2, S3, and Bedrock. For modeling and experiments, I use AnyLogic platforms, optimization packages, and internal tools. Beyond programming, I actively use visualization, documentation, and scenario comparison tools. Modeling is as much about storytelling as it is about computation.

The biggest challenge is managing system complexity without oversimplifying reality. Large systems exhibit nonlinear behavior, feedback loops, and delays. A model that is too simple is misleading, while one that is too complex becomes impractical.

Another challenge is alignment. Since models influence decisions across many teams, it is crucial to ensure a shared understanding and trust in the results.

Three skills are especially important for success in this role:
1. Systems thinking—understanding how local decisions affect global outcomes.
2. Structured problem-solving—breaking down uncertainty into manageable components.
3. Communication—translating analytical insights into business language. 

Amazon actively encourages continuous learning. I have benefited from internal training, participation in cross-functional projects, and experimentation with new technologies, including generative AI.

Equally important is gaining experience through taking ownership—being trusted to lead ambitious projects and learning through practice.

Working in an international team is intellectually stimulating. You constantly encounter different approaches to problem-solving, communication, and risk assessment. This forces clear thinking and respectful collaboration.

The main advantage of international teams is diversity of thought; the main drawback is the risk of misalignment if assumptions are not explicitly stated. Therefore, clear communication is extremely important.

One memorable moment was watching a model I helped build evolve from a prototype into a working system that influences daily decisions at a company-wide scale. Seeing abstract assumptions turn into real results is motivating. My models allow customers to receive packages faster and cheaper, which brings me professional satisfaction.

Success in this field requires curiosity, humility, and resilience. Models will be wrong, assumptions will break, and reality will surprise you. The key is to learn faster than the system changes.

About Life Abroad

In my case, the experience unfolded at different stages of my career. For long-term positions, it is important first to ensure job stability, while education and experience abroad can significantly expand opportunities.

Amazon provides structured support for relocation, including visa assistance, relocation packages, and onboarding, which significantly reduces the difficulties of moving.

The work culture differs from my home country: decisions are data-driven, expectations are explicitly documented, and ownership is valued over hierarchy.

My advice to those planning to live and work abroad: be patient with yourself. Cultural adaptation takes time, so develop language skills, build local networks, and remain open-minded. Living abroad is not just a career step, but also personal growth.