Amazon’s Last Mile Team is looking for a passionate individual with strong optimization and analytical skills to join its Last Mile Science team in the endeavor of designing and improving the most complex planning of delivery network in the world.
Last Mile builds global solutions that enable Amazon to attract an elastic supply of drivers, companies, and assets needed to deliver Amazon's and other shippers' volumes at the lowest cost and with the best customer delivery experience.
Last Mile Science team owns the core decision models in the space of jurisdiction planning, delivery channel and modes network design, capacity planning for on the road and at delivery stations, routing inputs estimation and optimization.
Our research has direct impact on customer experience, driver and station associate experience, Delivery Service Partner (DSP)’s success and the sustainable growth of Amazon.
Optimizing the last mile delivery requires deep understanding of transportation, supply chain management, pricing strategies and forecasting.
Only through innovative and strategic thinking, we will make the right capital investments in technology, assets and infrastructures that allows for long-term success.
Our team members have an opportunity to be on the forefront of supply chain thought leadership by working on some of the most difficult problems in the industry with some of the best product managers, scientists, and software engineers in the industry.
Key job responsibilities
Candidates will be responsible for developing solutions to better manage and optimize delivery capacity in the last mile network.
The successful candidate should have solid research experience in one or more technical areas of Operations Research or Machine Learning.
These positions will focus on identifying and analyzing opportunities to improve existing algorithms and also on optimizing the system policies across the management of external delivery service providers and internal planning strategies.
They require superior logical thinkers who are able to quickly approach large ambiguous problems, turn high-level business requirements into mathematical models, identify the right solution approach, and contribute to the software development for production systems.
To support their proposals, candidates should be able to independently mine and analyze data, and be able to use any necessary programming and statistical analysis software to do so.
Successful candidates must thrive in fast-paced environments, which encourage collaborative and creative problem solving, be able to measure and estimate risks, constructively critique peer research, and align research focuses with the Amazon's strategic needs.
As a senior scientist, you will also help coach / mentor junior scientists in the team.
We are open to hiring candidates to work out of one of the following locations :
Austin, TX, USA Bellevue, WA, USA Virtual Location - USA
BASIC QUALIFICATIONS
- 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
- 5+ years developing optimization and machine learning algorithms to solve real world business problems
- Proficiency in problem scoping, model development, model validation and model implementation into production
- Proficiency working with Python and other high-level languages like Java / C++ / Scala with at least 5 years of coding experience
- Strong fundamentals in problem solving, algorithm design and complexity analysis.
- Proven track in leading, mentoring, and growing teams of scientists.
- Excellent written and verbal communication skills with technical and business teams; ability to speak at a level appropriate for the audience.
- The ideal candidate can present business cases and document the models and analysis and present the results in order to influence important decisions.
PREFERRED QUALIFICATIONS
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Bedrock : Model Architecture and Optimization, Application Integration, agent creation and orchestration.
- LLM models : Using GPT and Claude models, Text Generation and Manipulation, Prompt Tuning and Orchestration, Model Fine Tuning.
- Langchain : Orchestration, App Building, Model Pipeline Creation and Maintenance.
- Problem domains : Categorization, Programming, Text Generation, Classification, Automation.
- Machine Learning.
- Deep Learning.
- Linear Programming.
- Quadratic optimization.
- Dynamic Programming.
- Monte Carlo simulations.
- Time Series Forecasting.
- Geospatial Modeling.
- Data modeling, Data Analytics.
- Statistics, Design of Experiments, A / B Testing.
- Languages : C, Matlab, TOAD, R, Tableau, Tibco Spotfire, Excel and vbscript. Python (XGBoost, H2O, Scikit Learn, Numpy, Pandas, NLTK, Stats models, Deep Learning), Natural Language Processing.
SQL, JMP / JSL (by SAS), Streamlit. Methods such as parallelization, vectorization, multiprocessing, concurrency, batch computing.
- AWS : EC2, Lambda, Batch, SageMaker, Redshift, Athena, Data wrangler, boto3, SQS, SNS, IAM, etc.
- Technologies : Network optimization, Mixed Integer programming, Causal inference, etc.
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