Senior Applied Scientist, Middle Mile
DESCRIPTION
Amazon’s Middle Mile Planning & Optimization team is looking for an exceptional Sr. Applied Scientist to solve complex optimization problems that ensure we exceed customer delivery promise expectations and minimize overall operational cost while supporting Amazon’s rapid growth globally. We use cutting edge technologies in large-scale optimization, predictive analytics, and generative AI to optimize the flow of packages within our network to efficiently match network capacity with shipment demand. Our services already handle thousands of requests per second, make business decisions impacting billions of dollars a year, and improve the delivery experience for millions of online shoppers. That said, this remains a fast-growing business and our journey has just started. Our mission is to build the most efficient and optimal transportation solution on the planet, using our technology and engineering muscle as our biggest advantage.
Key job responsibilities
You will work closely with product managers, research scientists, business/operations leaders, and technical leadership to build capabilities that transform our transportation network. This includes analyzing big data, building end-to-end workflows, prototype optimization/simulation models, and launch production capabilities. You will have exposure to senior leadership as you communicate results and provide scientific guidance to the business. Your insights will be a key influencer of our product strategy and roadmap and your experimental research will inform our future investment areas.
About the team
You will join the Surface Research Science (SRS) team, which is the science partner of the Middle-Mile Planning & Optimization tech organization. SRS is working on a fascinating range of problems, including some of the hardest and largest optimization, simulation, and prediction problems in the industry. Examples are long-term and short-term demand forecasting, capacity planning, driver scheduling, vehicle routing, and equipment rebalancing problems.
We are open to hiring candidates to work out of one of the following locations:
Bellevue, WA, USA
BASIC QUALIFICATIONS
- 3+ years of building machine learning models or developing algorithms for business application experience
- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field, or Master's degree and 10+ years of industry or academic research experience
- Knowledge of programming languages such as C/C++, Python, Java or Perl
- Experience in large-scale linear and non-linear optimization as well as relevant decomposition techniques
PREFERRED QUALIFICATIONS
- Experience in network topology optimization, network flow optimization, FTL/LTL route plan optimization, and other related transportation planning problems.
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. This position will remain posted until filled. Applicants should apply via our internal or external career site.