Applied Scientist II, Amazon

This role is to solve business problems in Machine Learning for the Seller and Fulfilment Tech (SFT) org, which is poised to build solutions to serve a population of 1.9B (Emerging Countries), and cater to the next 500MM+ customers.
The overarching goal of the team is to enhance ML expertise and fluency within SFT and across IST, championing engineering and operational excellence in ML model development and other related parts of the ML model lifecycle. Some of the key areas which the team owns in this space area:
Selection Recommendations, Registration improvements, Bad actor detection and prevention
Selection economics, Inventory recommendation, Delivery Promise Predictions, Seller success.
Within the ML space, the AS II leader would have to solve intrinsically hard problems where neither problem nor solution is well defined. So, the leader should have high focus on building a deep understanding of the ML science space, experimentation methodology, as well as a high focus on embracing external trends, especially applications of GenerativeAI and LLMs.
A large focus area for the role is to also contribute towards the science and research aspects. The ASII leader applies and extends existing scientific techniques, and invents new ones to address specific customers’ needs or business problems, at a project level. This should also lead to regular contributions to internal or external peer-reviewed publications that validate novelty
Key job responsibilities
- Process and analyze large data sets using as many techniques as necessary
- Deliver scalable models that can analyze large data sets efficiently
- Build mathematical models to detect and classify specific data elements with high accuracy
- Prototype these models by using high-level modeling languages such as R or in software languages such as Python. A software team will be working with you to transform prototypes into production.
- Create, enhance, and maintain technical documentation, and present to other scientists and business leaders.
- 3+ years of building models for business application experience
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
- Experience using Unix/Linux
- Experience in professional software development