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Applied Scientist I, Amazon
3 months ago
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.
BASIC QUALIFICATIONS
- Experience programming in Java, C++, Python or related language
- Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse
- Experience building machine learning models or developing algorithms for business application
PREFERRED QUALIFICATIONS
- Experience implementing algorithms using both toolkits and self-developed code
- Have publications at top-tier peer-reviewed conferences or journals
- Experience in building speech recognition, machine translation and natural language processing systems (e.g., commercial speech products or government speech projects)
- Master's degree in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field