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# Data Scientist II - AMZ27022.1

- **Company:** Amazon.com, Inc.
- **Location:** San Diego, CA, United States
- **Salary:** $136,000.0 - $184,000.0
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Artificial Neural Networks, Big Data, Extract Transform Load (ETL), Data Migration, Genetic Algorithm, Python (Programming Language), MATLAB, Machine Learning, Natural Language Processing, Network Model, Oracle (Applications), SQL Databases, Apache Spark, Model Validation, Information Technology, Data Analytics, Amazon Redshift
- **Published:** July 19, 2026
- **Apply:** https://dejobs.org/x/x/C24DAB05C9A74E4FB00DC31089865122/job/

## About the Role

Master's degree or foreign equivalent degree in Statistics, Applied Mathematics, Economics, Engineering, Computer Science, or a related field and one year of experience in the job offered or a related occupation. Employer will accept a Bachelor's degree or foreign equivalent degree in Statistics, Applied Mathematics, Economics, Engineering, Computer Science, or a related field and five years of progressive post-baccalaureate experience in the job offered or a related occupation as equivalent to the Master's degree and one year of experience. Must have one year of experience in the following skill(s): (1) building statistical models and machine learning models using large datasets from multiple resources; (2) writing SQL scripts for analysis and data migration; and (3) applying specialized modelling software including R, Python, or MATLAB.

## Description

Design and implement scalable and reliable approaches to support or automate decision making throughout the business. Apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear. Acquire data by building the necessary SQL / ETL queries. Import processes through various company specific interfaces for accessing Oracle, RedShift, and Spark storage systems. Build relationships with stakeholders and counterparts. Analyze data for trends and input validity by inspecting univariate distributions, exploring bivariate relationships, constructing appropriate transformations, and tracking down the source and meaning of anomalies. Build models using statistical modeling, mathematical modeling, econometric modeling, network modeling, social network modeling, natural language processing, machine learning algorithms, genetic algorithms, and neural networks. Validate models against
alternative approaches, expected and observed outcome, and other business defined key performance indicators. Implement models that comply with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of production.


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