Data Scientist II, Rapid & Rural Logistics (R2L) Analytics

The R2L team is responsible for building the next generation supply chain for Amazon’s world-class ultra-fast customer experiences including Amazon Fresh groceries, Sub-Same Day, Amazon Now, and other soon-to-launch exciting new businesses. Join us and you'll be taking part in serving our customers in as fast as 30 minutes! We are looking for a Data Scientist to join our team and solve some of the most complex business problems! Key job responsibilities - Work with product managers, engineers, other scientists, and leadership to identify and prioritize complex problems. - Interview stakeholders to gather business requirements and translate business problems into data science or analytical problems - Design, develop, and evaluate highly innovative statistics and ML models - Guide and establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Proactively seek to identify business opportunities and insights and provide solutions to shape key business processes and policies based on a broad and deep knowledge of Amazon data, industry best-practices, and work done by other teams. A day in the life In this role, you will be a technical expert with significant scope and impact. You will work with Product and Supply Chain Managers, Business Intelligence Engineers, System Developers and other Scientists, to develop models that solve a wide range of complex and ambiguous business problems, with the main goal to improve customer experience, improve availability of products and reduce supply chain cost. A successful Data Scientist will have bias for action needed in a startup environment, with great leadership skills, proven ability to build and manage medium-scale modeling projects, identify data requirements, build methodology and tools that are statistically grounded. It will be a person who enjoys diving deep into data, doing analysis, discovering root causes, and designing long-term scientific solutions. We are seeking someone who can thrive in a fast-paced, high-energy and fun work environment where we deliver value incrementally and frequently. We look for individuals who know how to deliver results and show a desire to develop themselves, their colleagues, and their career. Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: - Medical, Dental, and Vision Coverage - Maternity and Parental Leave Options - Paid Time Off (PTO) - 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team The R2L team is responsible for building the next generation supply chain for Amazon’s world-class ultra-fast customer experiences including Amazon Fresh groceries, Sub-Same Day, Amazon Now, and other soon-to-launch exciting new businesses. Join us and you'll be taking part in serving our customers in as fast as 30 minutes! Minimum qualifications - Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (STEM) - 2+ years of data scientist experience - 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience - 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience - 1+ years of working with or evaluating AI systems experience - Experience applying theoretical models in an applied environment - Experience effectively communicating complex concepts through written and verbal communication Preferred qualifications - Ph.D. in Science, Technology, Engineering, or Mathematics (STEM) - Knowledge of machine learning concepts and their application to reasoning and problem-solving - Experience in Python, Perl, or another scripting language - Experience in a ML or data scientist role with a large technology company - Experience in defining and creating benchmarks for assessing GenAI model performance - Experience working on multi-team, cross-disciplinary projects - Experience applying quantitative analysis to solve business problems and making data-driven business decisions Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits . USA, WA, Bellevue - 136,000.00 - 184,000.00 USD annually