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Data Scientist, LM Planning, LM Science & Analytics Automation

Amazon

Data Scientist, LM Planning, LM Science & Analytics Automation

full-timePosted: Aug 20, 2026Updated: Aug 27, 2026Bellevue, Washington, United States

Job Description

Have you wondered at the speed at which your Amazon purchase arrived at your door, in that box with a smile? wondered where it came from and how much it cost Amazon to deliver it to you? Amazon Last Mile Strategic Planning is looking for Sr. Data Scientists, developing solutions to optimize our delivery network topology, strategically maximizing Customer Experience and minimizing cost to serve and increase speed. You will partner with the Scientific community to help design optimization and strategy. You will also collaborate with technical teams developing automated tools for network flow and execution systems. You will work directly with business leadership and operational stakeholders to influence their strategy and gather inputs to solve problems. To be successful in the role, you will need deep analytical skills and a strong scientific background. The role also requires excellent communication skills, translating technical contents to business friendly narrative. Ability to influence across business functions at different levels, including your own team. You will work in a fast-paced environment that requires you to be detail-oriented and comfortable in working with data, science, business and technical teams. Key job responsibilities -Design and develop mathematical, statistical and optimization models to optimize Delivery Network Topology design.. -Manage several, high impact projects simultaneously -Consult and collaborate with business and technical stakeholders across multiple teams to define new opportunities to optimize Delivery Network Topology -Communicate data-driven insights and recommendations to diverse stakeholders through technical and/or business papers -Leverage LLMs to improve explainability of optimization and drive engagement from volume planning, demand planning stakeholders -Define measurement frameworks for optimization solutions where no prior art exists and own the scientific framework for ‘Topology Plans’ multi-contact journey -Choose the right methods (statistical, causal, ML, LLM, hybrid) for each problem and justify trade-offs. Drive excellence in evaluation: ground-truth construction with Quality auditors, human audits, precision/recall, drift, calibration, bias, safety, and cost - Design driver-analysis and bridging methods explaining KPI movement (WoW, MoM, YoY, vs OP2) across dimensions for "why" - Partner with teams in productionizing; Own AWS tech stack compliancy (Shepherd risk, App Security red-certification, Kale, Legal, Threat Models, for scientific assets) - Mentor team members; provide promotion assessments; contribute hiring at DS II and DS III. Represent LM Planning in the broader Amazon Data Science community - Produce design and technical documentation A day in the life Review current solutions, assumptions and question the status quo to find improvements. Drive technical partners adopt improvements, to increase coding velocity, accuracy, quality of scientific solutions. Research for reusable tools/techniques and translate adopting to About the team Last Mile is the final mile of Amazon purchase. We- LM Strategic Planning ‘design and plan the Delivery Station Network’. LM network continues to grow multi-fold in North America, AMET, Emerging Market countries, delivering better customer experience and speed to customers. This growth will help Amazon gain most of distribution network share, in every country across the world. LM Strategic Planning-Science Analytics & Automation provides foundational solutions for network expansion roadmap that looks a 1-5 years horizon.

Locations

  • Bellevue, Washington, United States

Salary

136,000 - 184,000 USD / yearly

Skills Required

  • machine learning conceptsintermediate
  • Pythonintermediate
  • definingintermediate

Required Qualifications

  • 2+ years of data scientist experience (experience, 2 years)
  • 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience (experience, 3 years)
  • 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience (experience, 3 years)
  • 1+ years of guiding and coaching a group of researchers experience (experience, 1 years)
  • 1+ years of working with or evaluating AI systems experience (experience, 1 years)
  • 1+ years of creating or contributing to mathematical textbooks, research papers, or educational content experience (experience, 1 years)
  • Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (STEM) (experience)
  • Experience applying theoretical models in an applied environment (experience)

Preferred Qualifications

  • Ph.D. in Science, Technology, Engineering, or Mathematics (STEM) (experience)
  • Knowledge of machine learning concepts and their application to reasoning and problem-solving (experience)
  • Experience in Python, Perl, or another scripting language (experience)
  • Experience in defining and creating benchmarks for assessing GenAI model performance (experience)
  • Experience applying quantitative analysis to solve business problems and making data-driven business decisions (experience)
  • Experience effectively communicating complex concepts through written and verbal communication (experience)

Responsibilities

  • Design and develop mathematical, statistical and optimization models to optimize Delivery Network Topology design..
  • Manage several, high impact projects simultaneously
  • Consult and collaborate with business and technical stakeholders across multiple teams to define new opportunities to optimize Delivery Network Topology
  • Communicate data-driven insights and recommendations to diverse stakeholders through technical and/or business papers
  • Leverage LLMs to improve explainability of optimization and drive engagement from volume planning, demand planning stakeholders
  • Define measurement frameworks for optimization solutions where no prior art exists and own the scientific framework for ‘Topology Plans’ multi-contact journey
  • Choose the right methods (statistical, causal, ML, LLM, hybrid) for each problem and justify trade-offs. Drive excellence in evaluation: ground-truth construction with Quality auditors, human audits, precision/recall, drift, calibration, bias, safety, and cost
  • Design driver-analysis and bridging methods explaining KPI movement (WoW, MoM, YoY, vs OP2) across dimensions for "why"
  • Partner with teams in productionizing; Own AWS tech stack compliancy (Shepherd risk, App Security red-certification, Kale, Legal, Threat Models, for scientific assets)
  • Mentor team members; provide promotion assessments; contribute hiring at DS II and DS III. Represent LM Planning in the broader Amazon Data Science community
  • Produce design and technical documentation

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