Data Scientist II: Scalable ML for Business Decisions

Amazon Science; Culver City (CA); On-site; USD 159,000 - 184,000; Full time

Job summary

Amazon.com Services LLC in Culver City, California is seeking Data Scientist II to design and implement scalable approaches supporting decision making across the business. You will apply a range of data science techniques, acquire data via SQL/ETL, and interface with Oracle, RedShift, and Spark storage systems to build robust models.

The role involves building and validating models across statistical, econometric, NLP, and machine learning methods, ensuring production-grade ETL reliability and

Qualifications

• Master’s degree or foreign equivalent 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.
• Bachelor’s degree or foreign equivalent with five years of progressive post-baccalaureate experience is equivalent to the Master’s degree and one year of experience.

Responsibilities

• Design and implement scalable approaches to support or automate decision making across the business.
• Acquire data by building necessary SQL/ETL queries and access Oracle, RedShift, and Spark storage systems.
• Build models using statistical, econometric, network, NLP, machine learning, genetic, and neural network techniques.
• Validate models against alternative approaches, expectations, and key performance indicators.

Skills

Statistical modeling

SQL scripting

R, Python or MATLAB

Education

Master’s degree in Statistics / Applied Math / Economics / Engineering / CS or related field

Tools

R

Python

MATLAB

Job description

Amazon.com Services LLC in Culver City, California is seeking Data Scientist II to design and implement scalable approaches supporting decision making across the business. You will apply a range of data science techniques, acquire data via SQL/ETL, and interface with Oracle, RedShift, and Spark storage systems to build robust models.

The role involves building and validating models across statistical, econometric, NLP, and machine learning methods, ensuring production-grade ETL reliability and