At Amazon, we strive to be the most innovative and customer centric company on the planet. Come work with us to develop innovative Global Transportation Products and research driven solutions in a fast-paced environment by collaborating with smart and passionate product leaders, researchers and software developers.
Our mission is to build the most efficient and intelligent transportation solutions on the planet.
The Middle Mile Research, Planning & Optimization (MMRPO) team is part of Amazon's Delivery Technology organization, and is responsible for deciding what physical resources (for example, trailers) we need on a week-by-week basis to operate our network.
Consuming inputs including demand forecasts and transportation network structure, our system automatically decides the optimal number of resources to have on-hand.
As a Data Scientist in MMRPO, you will operate at the crossroads of multiple advanced Amazon systems, getting global visibility of how Amazon moves inventory across our network and serves our customers.
You will enable the creation of products that drive ever-greater automation, scalability and optimization of every aspect of transportation, removing cost and delivering speed of execution for our customers.
The impact of your work will be global.
Data Scientist Responsibilities
As a successful Data Scientist in MMRPO, you need to be voraciously curious about Amazon's transportation operations and how data consumed and produced by our systems can be used to improve outcomes and lower costs.
Your responsibility is to expose and measure the current performance of our systems, find and quantify opportunities for improvement, and dive deep into existing algorithms to explain unexpected performance or measure causal relationships.
You will collaborate with engineering, research, and business teams for future innovation. You need to be a sophisticated user of data querying tools and advanced quantitative and modeling techniques, and an expert at synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication.
Major responsibilities include :
Translate business questions and concerns into specific quantitative questions that can be answered with available data using sound methodologies.
In cases where questions cannot be answered with available data, work with engineers to produce the required data.
a. Retrieve, synthesize, and present critical data in a format that is immediately useful to answering specific questions or improving system performance.
b. Analyze historical data to identify trends and support decision making.
c. Apply statistical or machine learning knowledge to specific business problems and data.
d. Improve upon existing methodologies by developing new data sources, testing model enhancements, and fine-tuning model parameters.
e. Provide requirements to develop analytic capabilities, platforms, and pipelines.
a. Formalize assumptions about how Middle Mile systems are expected to work, create statistical definitions of outliers, and develop methods to systematically identify these outliers.
Work out why such examples are outliers and define if any actions are needed.
b. Given anecdotes about anomalies, design strategies to quantify the overall impact of such anomalies, deep dive to explain why they happen, and identify fixes.
a. Build decision-making models and propose solutions for business problems.
b. Conduct written and verbal presentations to share insights and recommendations to audiences of varying levels of technical sophistication.