Amazon.com Services LLC

Data Science, Science, operations

DataScientistI,DemandForecasting

$130–160k Bellevue, Washington, United States FULL TIME
Market Sentiment
HIGH DEMAND

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

The Brief

“Data Scientist I, Demand Forecasting at Amazon.com Services LLC. Skills: Demand Forecasting, Machine Learning, Causal Inference, Experimentation. Design experiments. Analyze experiments”

What You'll Achieve.

Delight customers; Improve forecast accuracy; Quantify impact of initiatives

Industry & Context.

Data Science, Science, operations
Problems you'll solve

Analytical abilities; Ambiguous forecasting questions

What They're Looking For.

Must Have

3+ years data querying languages experience, 3+ years scripting languages experience, 3+ years statistical software experience, 3+ years data scientist experience, 3+ years quantitative analyst experience, Bachelor's degree, Familiarity with LLMs or GenAI

Nice to Have

Knowledge of statistical packages, Knowledge of business intelligence tools, Experience with clustered data processing, Experience with time series forecasting, Experience with demand modeling, Experience with bias correction techniques

What You'll Do.

Measure forecast model impact

Measure SCOT initiative impact

Draw causal inferences

Develop bias correction models

Improve forecast accuracy

Contribute to GenAI/LLM research

Enhance Labs experimentation platform

Build inference solutions

Build measurement solutions

Quantify forecasting improvements

Work horizontally across portfolio

Collaborate with product managers

Collaborate with applied scientists

Collaborate with engineering teams

Embed analytics solutions

Address forecasting questions

Model demand prediction

Communicate measurement results

Translate technical frameworks

Provide business-oriented insights

Provide actionable recommendations

Extract insights from data

How You'll Work.

Team & Collaboration

Product managers; Applied scientists; Engineering teams

Communication Scope

Communicate results; Business-oriented insights; Actionable recommendations

Full Job Description

Amazon has the world's most complex supply chain: we fulfill global demand for hundreds of millions of products at lightning fast delivery speeds. We need your skills to optimize our supply chain, with the end goal of delighting our customers. A core part of the supply chain operations is Demand Forecasting: We forecast the demand of tens of millions of products. These forecasts are used to make many decisions, such as automatically order hundreds of millions worth of inventory, decide where to place that inventory, and establish labor plans for hundreds of warehouses. The Product Data Science team within Forecasting & Labs (part of Supply Chain Optimization Technologies) is looking for an analytical and technically skilled Data Scientist to join our team. Our team is responsible for bias correction model development and A/B testing for forecast improvements, GenAI/LLM research for forecast explainability, and deep analytics for Labs and Foundation Models. We work horizontally across the forecasting product portfolio—including National, Regional, Grocery, SSD, Inbound, and CIV forecasting—to embed advanced analytics and machine learning solutions where they create the most value. This position will be responsible for developing and supporting data science methodologies and building models to address ambiguous forecasting questions. The Data Scientist will design and analyze experiments (A/B tests) to measure the impact of forecast model changes, develop bias correction models to improve forecast accuracy, and contribute to GenAI/LLM-based approaches for forecast explainability and interpretability. The role also involves supporting the Labs experimentation platform, which designs and executes inference and experimentation systems that measure the impact of initiatives across SCOT. The Data Scientist needs to be familiar with deriving causal inferences using observational and experimental data and able to model variations related to demand prediction, out of stock, s

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