Retail Pricing Science is a centralized machine learning team on a strategic mission to develop models, solutions, and platforms that drive improvements to both the competitiveness and perception of prices for all products sold on Amazon worldwide.
We are on the hunt for exceptional Applied Machine Learning Scientists to innovate the ways in which we model prices, predict product demand, and adapt to constantly shifting marketplace dynamics, all while scaling to hundreds of millions of products globally.
You will partner with technology and product leaders to understand and translate key requirements, propose appropriate algorithmic solutions, and successfully drive the creation of positive customer and business impact.
· Translate business requests into specific quantitative research questions that can be answered with available data using sound methodologies. Work with data and software engineers to define and capture the required data when not initially available.
· Improve upon existing methodologies by developing new models, features, preprocessing and training approaches, and evaluation metrics.
· Analyze historical data to support decision making, and identify opportunities for further impact and/or model improvement.
· Conduct written and verbal presentations to share insights and recommendations to audiences of varying levels of technical sophistication.
· Ph.D./M.S. in Computer Science, Machine Learning, Operational Research, Statistics or a related quantitative field
· 2+ years of full time hands-on employment or postdoc experience building and validating predictive machine learning models
· 2+ years hands-on experience in Python, Scala, Java, C#, C++ or other similar languages
· 1+ years professional experience in software development
· Ability to convey algorithmic insights to a diverse set of technical and non-technical audiences
· Ph.D. in Computer Science, Machine Learning, Operational Research, Statistics or a related quantitative field
· Financial quantitative analysis or market making experience
· Significant peer-reviewed scientific contributions in premier journals and conferences
· Strong fundamentals in data structures, problem solving, algorithm design and complexity analysis
· Experience working with a broad set of linear and non-linear machine learning algorithms, such as Gradient Boosting, Deep Learning, or Reinforcement Learning
· Experience working with multiple data modalities, including Images, Text, and Structured datasets
· Proven track record in technically leading and mentoring interns or junior scientists
· Superior verbal and written communication and presentation skills, ability to convey rigorous mathematical concepts and considerations to non-experts