Amazon's Choice uses machine-learning and big data to drastically simplify the shopping experience on Amazon, helping busy customers purchase high quality products that meet their needs, intuitively and with low risk of regret. By pioneering a recommendation system that picks only one product per need, it has struck a chord with customers, and is scaling rapidly as a result.
Highlighting only one product is intrinsically harder than picking N products (what recommenders usually do). It creates new challenges as customers put more trust in each pick (so the cost of being wrong increases exponentially), and unlocks new opportunities as more surface area becomes available to address the nuances of each shopping request. The problem increases in complexity as Amazon's Choice highlights products in billions of shopping journeys each month across all marketplaces WW. This is a rich space for data science & analytics to look around corners, identify step-change opportunities to improve the accuracy of Amazon's Choice and customer experience, and use machine-learning techniques and statistical models to solve high impact complex science problems.
As a Head of Data Science & Analytics for Amazon's Choice, you bridge the gap between science, tech and business. You lead a cross-functional team of data scientists, BI resources and data engineers, and partner with team's leadership and stakeholders (applied scientists, economists, software engineers and product managers) to design, implement and deliver ground-breaking data science solutions, such as a new machine learning model for a critical area of Amazon's Choice. You make sure your team has enough specificity to build the right solutions, and can discern which features are essential. You keep an eye for extensibility and scalability. You proactively “re-structure” data sources to unlock opportunities. You lead projects to improve data science and operational excellence best practices on your team. You are hands-on when need be, while being strategic about developing your talents. You are perpetually curious about new science techniques, and how to apply them in your space.
· Masters degree in a quantitative field (economics, computer science, statistics, or equivalent), or PhD in a discipline such as chemistry or biology
· Skills with programming language like R, Python and/or Scala or similar scripting language.
· 7+ years experience applying ML to solve complex problems for large-scale applications
· 3+ years experience managing data scientists and data specialists
· PhD in a quantitative field (economics, computer science, statistics, or equivalent)
· Expert in more than one more major programming languages (Java, C++ or similar) and at least one scripting language (Python, or similar).
· 10+ years of experience applying theoretical models in an applied environment, with successful experience building AI algorithms, preferably proved with relevant patent listed as inventor.
· Proven experience building and managing a highly efficient cross-functional science & analytics team.
· Great verbal and written communication and presentation skills, ability to convey rigorous mathematical concepts and considerations to non-experts.