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Senior Optimization Scientist, Last Mile Logistics
2 months ago
Amazon's Last Mile Team is in search of a dedicated professional with exceptional analytical and optimization capabilities to enhance its Last Mile Science division. This team is committed to designing and refining the intricate planning of the world's most sophisticated delivery network. The Last Mile division develops global solutions that empower Amazon to secure a flexible supply of drivers, companies, and resources essential for delivering both Amazon's and other shippers' volumes at minimal costs while ensuring an outstanding customer delivery experience.
Key Responsibilities
The selected candidate will be tasked with creating solutions aimed at improving and managing delivery capacity within the last mile network. A strong background in Operations Research or Machine Learning is essential. The role will involve identifying and evaluating opportunities to enhance existing algorithms and optimizing system policies related to the management of external delivery service providers and internal planning strategies. Candidates should possess superior logical reasoning skills, enabling them to tackle large, ambiguous challenges, translate high-level business requirements into mathematical models, and contribute to the software development of production systems.
To support their initiatives, candidates must independently analyze data and utilize necessary programming and statistical analysis tools. Successful candidates will thrive in dynamic environments that promote collaborative and innovative problem-solving, assess and estimate risks, provide constructive feedback on peer research, and align their research objectives with Amazon's strategic goals. As a senior scientist, mentoring and guiding junior scientists will also be part of your responsibilities.
Basic Qualifications
- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics, or a related quantitative field, or a Master's degree with 10+ years of relevant industry or academic research experience.
- 5+ years of experience in developing optimization and machine learning algorithms to address real-world business challenges.
- Expertise in problem scoping, model development, validation, and implementation into production.
- Proficiency in Python and other high-level programming languages such as Java, C++, or Scala, with a minimum of 5 years of coding experience.
- Strong foundation in problem-solving, algorithm design, and complexity analysis.
- Demonstrated experience in leading, mentoring, and developing teams of scientists.
- Excellent written and verbal communication skills, capable of engaging with both technical and business teams.
The ideal candidate will effectively present business cases, document models and analyses, and communicate results to influence key decisions.
Preferred Qualifications
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, TensorFlow, NumPy, and SciPy.
- Familiarity with Bedrock: Model Architecture and Optimization, Application Integration, agent creation, and orchestration.
- Experience with LLM models: Utilizing GPT and Claude models, Text Generation and Manipulation, Prompt Tuning and Orchestration, Model Fine Tuning.
- Knowledge of Langchain: Orchestration, App Building, Model Pipeline Creation and Maintenance.
- Expertise in problem domains including Categorization, Programming, Text Generation, Classification, and Automation.
- Proficiency in Machine Learning, Deep Learning, Linear Programming, Quadratic Optimization, Dynamic Programming, Monte Carlo Simulations, Time Series Forecasting, and Geospatial Modeling.
- Experience with Data Modeling, Data Analytics, Statistics, Design of Experiments, and A/B Testing.
- Familiarity with programming languages and tools such as C, Matlab, TOAD, R, Tableau, Tibco Spotfire, Excel, and vbscript.
- Experience with AWS services such as EC2, Lambda, Batch, SageMaker, Redshift, Athena, Data Wrangler, boto3, SQS, SNS, and IAM.
- Knowledge of technologies related to Network Optimization, Mixed Integer Programming, and Causal Inference.
Diversity and Inclusion
Amazon is committed to fostering a diverse and inclusive workplace. We are an equal opportunity employer and do not discriminate based on race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
Compensation
Our compensation reflects the cost of labor across various US geographic markets. The base pay for this position ranges from $150,400/year in our lowest geographic market up to $260,000/year in our highest geographic market. Pay is influenced by factors such as market location and may vary based on job-related knowledge, skills, and experience. Amazon offers a comprehensive compensation package, which may include equity, sign-on payments, and a full range of medical, financial, and other benefits.
