Amazon.com Services LLC

Data Science, Science, transportation and logistics

DataScientist

$136–184k Bellevue, Washington, United States FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid+ candidates.

The Brief

“Data Scientist at Amazon.com Services LLC. Skills: Machine learning, Time series forecasting, Operations research, Logistics optimization. Apply ML, statistical modeling, time series analysis, and. Build solutions for delivery routing”

What You'll Achieve.

Improve cost-to-serve; Improve customer experience

Industry & Context.

Data Science, Science, transportation and logistics
Problems you'll solve

Solve logistics challenges; Solve fulfillment challenges

What They're Looking For.

Must Have

Master's degree in STEM, 2+ years data scientist experience, 3+ years SQL, Python, or R experience, 3+ years machine learning/statistical modeling experience, 1+ years guiding researchers, 1+ years evaluating AI systems, 1+ years creating educational content

Nice to Have

PhD in STEM, Knowledge of ML concepts, Experience in Python, Perl, Experience in ML/data scientist role, Experience defining GenAI benchmarks, Experience on multi-team projects, Experience applying quantitative analysis, Experience communicating complex concepts

What You'll Do.

Build solutions for delivery routing

Build solutions for capacity planning

Build solutions for demand forecasting

Build solutions for workforce scheduling

Build solutions for network optimization

Analyze operational data

Surface efficiency patterns

Surface emerging trends

Improve cost-to-serve

Improve customer experience

Partner with cross-functional teams

Implement data-driven strategies

Build scalable pipelines

Automate data ingestion

Automate feature engineering

Automate model training

Automate model validation

Monitor model performance

How You'll Work.

Team & Collaboration

Partnering with scientists; Partnering with engineers; Partnering with business stakeholders; Cross-functional teams; Small, collaborative team; Worldwide group

Communication Scope

Presenting findings

Full Job Description

Are you passionate about applying machine learning, time series forecasting, and operations research to transform the delivery of heavy and bulky items for Amazon customers? Are you excited about working with large-scale operational data and developing models that drive real business impact? If so, the Amazon Extra Large (AMXL) Science team may be the right fit for you. AMXL is Amazon's specialized business for delivering heavy and bulky items — appliances, furniture, fitness equipment, and mattresses — with a premium customer experience that includes room-of-choice delivery, at-home installations, and assembly services. In this role, you will leverage large-scale operational data to develop and deploy predictive models and optimization solutions that solve real-world logistics and fulfillment challenges, partnering closely with scientists, engineers, and business stakeholders. Key job responsibilities Apply machine learning, statistical modeling, time series analysis, and operations research techniques to build solutions for delivery routing, capacity planning, demand forecasting, workforce scheduling, and network optimization Analyze large-scale historical and real-time operational data to surface efficiency patterns, bottlenecks, and emerging trends across the AMXL network Develop, validate, and deploy models that improve cost-to-serve and customer experience Partner with cross-functional teams to implement data-driven strategies and measure impact Build scalable, automated pipelines for data ingestion, feature engineering, model training, and validation Monitor deployed model performance and communicate results through clear reporting on key operational and business metrics A day in the life You'll be part of a small, collaborative team of scientists who move fast and care deeply about the problems they solve. A typical week might involve whiteboarding a new forecasting approach with a senior scientist, partnering with engineers to push a model into production,

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