Amazon.com Services LLC

Data Science, Science, operations

DataScientistII,LongTermPlanningandForecasting

$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 II, Long Term Planning and Forecasting at Amazon.com Services LLC. Skills: Causal inference, Forecasting, Machine learning, Data science. Develop causal inference models. Develop automated explainability frameworks”

Industry & Context.

Data Science, Science, operations
Problems you'll solve

Reasoning; Problem-solving

What They're Looking For.

Must Have

2+ years of data scientist experience, 3+ years of data querying languages experience, 3+ years of scripting languages experience, 3+ years of statistical/mathematical software experience, 3+ years of machine learning/statistical modeling experience, 1+ years of guiding researchers experience, 1+ years of evaluating AI systems experience, 1+ years of creating mathematical textbooks experience, Master's degree in STEM, Experience applying theoretical models

Nice to Have

Ph.D. in STEM, Knowledge of machine learning concepts, Experience in Python, Experience in Perl, Experience defining GenAI benchmarks, Experience creating GenAI benchmarks, Experience communicating complex concepts

What You'll Do.

Develop causal inference models

Develop automated explainability frameworks

Develop variance bridging methodologies

Build automated Plan-vs-Actual models

Build automated Actual-vs-Actual models

Build and maintain causal model library

Develop GenAI-powered narrative generation capabilities

Design automated hypothesis ranking

How You'll Work.

Team & Collaboration

Cross-functional programs; Scientists; Economists; Engineers; Business customers; Operations teams; Stores teams; Finance teams

Communication Scope

Presenting to VP; Presenting to SVP; Written communication; Verbal communication

Process & Methodology

Cross-functional planning, Strategy workstreams, Program vision, Program strategy, Roadmap execution, Roadmap delivery, Planning calendars, Strategic review mechanisms, Organizational alignment

Full Job Description

We are seeking an experienced Data Scientist to drive scientific tooling supporting how Amazon's business customers interact with LTPF forecasts and plans. As a science leader within the LTPF, you will be responsible for building to the multi-year roadmap for customer engagement, ensuring that business stakeholders across Amazon can seamlessly access, understand, and act upon our forecasting outputs. In this role, you will manage the lifecycle of complex, cross-functional programs that transform how Operations, Stores, and Finance teams leverage LTPF insights for strategic decision-making. You will work with scientists, economists, engineers, and business customers to architect the customer interaction experience, including viewing capabilities, auditing tools, what-if analysis frameworks, and forecast intervention workflows. This role might be for you if you have interest and experience in: - Leading large, cross-functional planning and strategy workstreams that impact Amazon's topline growth - Defining multi-year program vision and strategy while balancing short-term execution - Regularly presenting to VP and SVP level leaders - Prioritizing operational excellence work alongside feature delivery on a roadmap - Showing strong business acumen with strategic, analytical, and critical thinking - Managing planning calendars and strategic review mechanisms - Driving organizational alignment across multiple teams and stakeholders Key job responsibilities As a Data Scientist in LTPF (Long-Term Planning & Forecasting): - You will develop causal inference models, automated explainability frameworks, and variance bridging methodologies that translate LTPF's forecasts and plans into actionable business intelligence. - Your work will enable leadership to understand why forecasts and actuals diverge, what is driving demand shifts, and how strategic decisions propagate through the planning ecosystem. - You will build automated Plan-vs-Actual and Actual-vs-Actual variance decompo

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