Data Scientist II: Modeling, ETL & Business Insights

Amazon Inc.; Seattle (WA); On-site; USD 175,000 - 213,000; Full time

Job summary

Amazon.com Services LLC is hiring a Data Scientist II in Sunnyvale, CA, to design and implement scalable data-driven solutions for business decisions. The role involves building ML models, performing extensive data analysis, and collaborating with stakeholders to ensure reliable ETL processes and accurate assessments of performance metrics.

The position requires advanced degree in a quantitative field, experience with SQL and data pipelines, and proficiency in statistical/machine learning tools.

Qualifications

• Master's degree or foreign equivalent in Statistics, Applied Mathematics, Economics, Engineering, Computer Science, or related field and 1 year of related experience; or Bachelor’s + 5 years of experience as equivalent.
• Experience building statistical models and machine learning models on large datasets from multiple resources; writing SQL scripts for analysis and data migration; using R, Python, or MATLAB.

Responsibilities

• Design and implement scalable approaches to support or automate decision making.
• Acquire data by building necessary SQL / ETL queries; access Oracle, RedShift, and Spark storage systems.
• Build models using statistical, econometric, and ML techniques; validate against KPIs and production constraints.
• Collaborate with stakeholders and ensure the reliability and efficiency of ETL processes at various stages of production.

Skills

Statistical modeling

Machine learning

Education

Master's degree in Statistics / Applied Math / Economics / Engineering / CS

Tools

R

Python

MATLAB

SQL

Job description

Amazon.com Services LLC is hiring a Data Scientist II in Sunnyvale, CA, to design and implement scalable data-driven solutions for business decisions. The role involves building ML models, performing extensive data analysis, and collaborating with stakeholders to ensure reliable ETL processes and accurate assessments of performance metrics.

The position requires advanced degree in a quantitative field, experience with SQL and data pipelines, and proficiency in statistical/machine learning tools.