- 8+ years of experience working in a combination of analytics, data science, machine learning, and software product development
- Strong coding and problem-solving skills in at least one programming language such as Python, Java, etc.; experience developing software (design and development life cycle)
- Strong communication, influencing and partnership skills; ability to work effectively across teams
The Amazon Topology team determines how many, what kind, and where to place new buildings for Amazon's supply chain worldwide. These facilities range from million-square-foot fulfillment centers with thousands of robots down to inner-city Prime Now facilities serving orders to be fulfilled within an hour. Each year we spend billions on these facilities and expect high-impact results from the network built upon them. That network is expanding faster than ever. This team owns the systems that are critical for managing capacity in Amazon's network for all retail customers on Amazon.com. If you are interested in diving into a multi-discipline, high impact space, this team is for you.
Unlike many companies who buy existing off-the-shelf planning systems, we create our systems entirely in-house. The Amazon Topology team is responsible for studying, designing, and building systems for the most challenging use cases in capacity planning today. We develop game-changing ideas and continuously improve them, resulting in sophisticated, intelligent, self-learning models. Our systems cover a broad range of solutions; we develop advanced mathematical models, data platforms, workflows, and front-ends using the latest technology frameworks and AWS. We develop demand forecasting algorithms, optimization models for various core decisions, machine learning techniques that create long term estimates and approximate network behavior, and systems that consider probability to simulate and analyze how our choices will perform. The systems we build are on the cutting edge of automated large-scale supply chain planning and optimization systems; we're unique in that we're simultaneously advancing the science of supply chain planning and solving some of the toughest computational challenges at Amazon.
We are looking for a Principal Data Scientist on this team. You will be responsible for identifying, scoping, and delivering capacity planning solutions with a focus on Europe; based on a deep understand of your customers' needs, you will work closely with senior leaders, scientists, engineers, and business teams worldwide to develop and implement advanced mathematical and economic models and algorithms. You will identify data and science-related bottlenecks, anticipate and make trade-offs, balance business needs versus scientific and technical complexity and constraints, and guide and manage escalations, collaborating closely with multiple teams to ensure the relevance and impact of your work to business stakeholders.
You will need an ability to take large, scientifically complex projects and break them down into manageable hypotheses, design meaningful research questions and analyze the resulting data to inform functional specifications, and then deliver features in a successful and timely manner. You excel at being a thought leader as we chart new courses with our capacity planning technologies, and at defining a vision for products in early stages. Maturity, high judgment, negotiation skills, and the ability to influence and earn the trust of senior leaders are essential to success in this role.
In this role, you will become an expert in the management of the most sophisticated supply chain in the world and develop a deep understanding of Amazon's retail business. You will design and architect systems and expand your technical depth and breadth while defining and driving key aspects of the customer experience on Amazon.com. You will influence the design of our core fulfillment network across multiple organizations and make decisions with billion-dollar impact to Amazon’s business.
- PhD in Machine Learning, Computer Science, AI, Computer Engineering, Statistics, Applied Mathematics, or a related field
- Extensive experience building predictive and optimization models and applying machine learning in a production environment, using techniques from classic time-series, Random Forest, ARIMA, regression to complex LSTM (long-short term memory) neural networks; experience with fully automated training (e.g. automatic re-training, automatic testing); experience with economic ROI analysis and with linear models to make big-impact decisions
- Experience with the end-to-end life cycle of data science and machine learning feature development (creating, prioritizing, and owning hypothesis and experiment backlogs; owning feature definition, roadmap development and prioritization)
- Ability to distill problem definitions, models, and constraints from informal business requirements
- Ability to convey rigorous mathematical and statistical concepts and considerations to non-experts
- Ability to deal with ambiguity and competing objectives; ability to balance technical leadership with sound business judgment
- A natural curiosity and desire to learn