Sr. Applied Scientist, Pricing Science

Amazon • Seattle, WA

5d

About The Position

We are looking for a talented, organized, and customer-focused applied researchers to join our Pricing Optimization science group, with a charter to measure, refine, and launch customer-obsessed improvements to our algorithmic pricing and promotion models across all products listed on Amazon. This role requires an individual with exceptional machine learning modeling and architecture expertise, excellent cross-functional collaboration skills, business acumen, and an entrepreneurial spirit. We are looking for an experienced innovator, who is a self-starter, comfortable with ambiguity, demonstrates strong attention to detail, and has the ability to work in a fast-paced and ever-changing environment. Key job responsibilities See the big picture. Understand and influence the long term vision for Amazon's science-based competitive, perception-preserving pricing techniques Build strong collaborations. Partner with product, engineering, and science teams within Pricing & Promotions to deploy machine learning price estimation and error correction solutions at Amazon scale Stay informed. Establish mechanisms to stay up to date on latest scientific advancements in machine learning, neural networks, natural language processing, probabilistic forecasting, and multi-objective optimization techniques. Identify opportunities to apply them to relevant Pricing & Promotions business problems Keep innovating for our customers. Foster an environment that promotes rapid experimentation, continuous learning, and incremental value delivery. Successfully execute & deliver. Apply your exceptional technical machine learning expertise to incrementally move the needle on some of our hardest pricing problems. A day in the life We are hiring a Sr. Applied Scientist to drive our pricing optimization initiatives. We drive cross-domain and cross-system improvements through: shape and extend our RL optimization platform - a pricing centric tool that automates the optimization of various system parameters and price inputs. Error detection and price quality guardrails at scale. Identifying opportunities to optimally price across systems and contexts (marketplaces, request types, event periods) Price is a highly relevant input into Stores architectures; this role creates the opportunity to drive extremely large impact (measured in Bs not Ms), but demands careful thought and clear communication.

Requirements

• 4+ years of applied research experience
• 3+ years of building machine learning models for business application experience
• PhD, or Master's degree and 6+ years of applied research experience
• Experience programming in Java, C++, Python or related language
• Experience with neural deep learning methods and machine learning
• Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
• Experience with large scale distributed systems such as Hadoop, Spark etc.

Responsibilities

• See the big picture. Understand and influence the long term vision for Amazon's science-based competitive, perception-preserving pricing techniques
• Build strong collaborations. Partner with product, engineering, and science teams within Pricing & Promotions to deploy machine learning price estimation and error correction solutions at Amazon scale
• Stay informed. Establish mechanisms to stay up to date on latest scientific advancements in machine learning, neural networks, natural language processing, probabilistic forecasting, and multi-objective optimization techniques. Identify opportunities to apply them to relevant Pricing & Promotions business problems
• Keep innovating for our customers. Foster an environment that promotes rapid experimentation, continuous learning, and incremental value delivery.
• Successfully execute & deliver. Apply your exceptional technical machine learning expertise to incrementally move the needle on some of our hardest pricing problems.
• shape and extend our RL optimization platform - a pricing centric tool that automates the optimization of various system parameters and price inputs.
• Error detection and price quality guardrails at scale.
• Identifying opportunities to optimally price across systems and contexts (marketplaces, request types, event periods)

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees