Lead Scientist in Retail Pricing Analytics
2 weeks ago
As a Senior Applied Scientist focusing on Pricing Analytics within the Product Intelligence division, you will spearhead the creation of advanced machine learning solutions. Your expertise will harness cutting-edge techniques in Large Language Models (LLMs) and multimodal approaches to refine product databases, assess entity relationships, execute entity linking, and normalize attributes, while employing sophisticated reasoning methods for enhanced product comprehension.
This pivotal role significantly impacts our core operations, ensuring the accuracy of information for a vast array of products on Amazon's platform, ultimately enhancing the shopping experience for millions of customers. The systems you develop will oversee Amazon's comprehensive product inventory, guaranteeing quality, accurate pricing distributions, and more.
We are in search of a seasoned scientist with profound knowledge of LLMs and NLP, alongside a solid grasp of traditional machine learning and quantitative techniques. The position also demands collaborative skills across various functions and a commitment to staying informed about the latest developments in Generative AI.
Key Responsibilities
- Design and implement systems that utilize LLMs and other methodologies to solve complex challenges in the pricing domain.
- Establish scientific benchmarks and maintain a broad perspective to shape Amazon's strategic vision for retail pricing analytics.
- Collaborate cross-functionally with diverse teams to ensure alignment of machine learning projects with business objectives and execute them effectively.
- Lead research initiatives and contribute to the publication of findings in esteemed academic conferences and journals.
The Retail Pricing Analytics team is a centralized, diverse group of STEM professionals dedicated to developing statistical, machine learning, reinforcement learning, optimization, and economic models that govern pricing for products sold globally by Amazon. The team emphasizes both competitiveness and long-term financial viability.
BASIC QUALIFICATIONSPhD or Master's degree with over 10 years of applied research experience
- Minimum of 4 years in applied research
- At least 4 years of experience in developing machine learning models for business applications
- Proficiency in programming languages such as Java, C++, Python, or similar
- Familiarity with neural deep learning techniques and machine learning methodologies.
PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics, or a related quantitative discipline
- Expertise in machine learning techniques for anomaly and error detection. Strong practical experience with deep learning frameworks like TensorFlow or PyTorch. Knowledge of weak supervision and semi-supervised learning techniques.
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