Data Scientist II - Amz9658237

Amazon Amazon · Big Tech · NY +1 · Corporate Operations

This role focuses on designing and implementing scalable data science approaches, acquiring and analyzing data, and building statistical and machine learning models. It involves using SQL for data acquisition and ETL, and implementing models with considerations for computational demands, accuracy, and reliability.

What you'd actually do

  1. Design and implement scalable and reliable approaches to support or automate decision making throughout the business.
  2. 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.
  3. Acquire data by building the necessary SQL / ETL queries.
  4. Import processes through various company specific interfaces for accessing Oracle, RedShift, and Spark storage systems.
  5. Build relationships with stakeholders and counterparts.

Skills

Required

  • 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.
  • 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.
  • building statistical models and machine learning models using large datasets from multiple resources
  • writing SQL scripts for analysis and data migration
  • applying specialized modelling software including R, Python, or MATLAB

What the JD emphasized

  • building statistical models and machine learning models using large datasets from multiple resources
  • writing SQL scripts for analysis and data migration
  • applying specialized modelling software including R, Python, or MATLAB

Other signals

  • 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.
  • Implement models that comply with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of production.