At Adaptive Transportation Optimization Service (ATROPS), we solve the transportation problems associated with moving an object from point A to point B in the best way for the customer, while minimizing transportation capacity risks, stress on our fulfillment operations, carbon impact, and cost. The Scientists at ATROPS are expected to innovate, tune, and evolve the portfolio of scientific models and solutions dedicated to outbound site and transportation route assignment.
In this position, you will be at the forefront of the science associated with the most innovative logistics operation in the world. Your opinions and skills will change the world of commerce. You will be surrounded by experts who are passionate about their ideas, and loved for your passion about yours.
Key job responsibilities
– Build and document subject matter expertise around mathematical research happening around Amazon Fulfillment Optimization
– Research and build solutions to optimization problems through mathematical models
– Bring your concepts to life through ownership and individual contribution of high number of detailed proposals, experimentation, data collection, and measured success
– Solve the biggest puzzle of all: explaining what is going on to internal customers and business partners
– Mentor junior engineers and scientists
– Partner with engineering teams who will integrate your models and solutions with systems, services, and integration gateways
– Make your work known and contribute to the field by producing publications
About the team
The Adaptive Load Balancing and Execution Decision Optimization (ALBEDO) team owns optimal site and shipment route assignment, modeling network utilization state and emitting important capacity-related signals, reducing strain on the transportation network, and minimizing churn throughout the fulfillment lifecycle. These solutions are driven by ML models and linear/integer programming systems, and exposed through a robust set of APIs and UI tools. While our end customer is the Amazon shopper, every Amazon outbound retail system is consumer of our data.
We are open to hiring candidates to work out of one of the following locations:
Austin, TX, USA | Bellevue, WA, USA
BASIC QUALIFICATIONS
– PhD, or Master’s degree and 6+ years of applied research experience
– Knowledge of programming languages such as C/C++, Python, Java or Perl
– Experience programming in Java, C++, Python or related language
– Experience with neural deep learning methods and machine learning
– Experience in building machine learning models for business application
– 4+ Years of experience building production mathematical programming solutions for a business domain
PREFERRED QUALIFICATIONS
– 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.
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $260,000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. Applicants should apply via our internal or external career site.