Data Scientist, Sheriff Team- Payroll tech -FinAuto
DESCRIPTION
Amazon strives to be Earth's most customer-centric company where people can find and discover anything they want to buy online. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated, and friendly work environment.
The FinAuto Payroll team, part of Finance Automation Org focuses on building applications with Gen-AI and machine learning models to improve payroll experience for Amazon employees. As a Data Scientist in the team, you will be working on building descriptive and predictive solutions to the development and business stakeholders through a combination of data mining techniques as well as statistical and machine learning techniques for segmentation and prediction. You will need to collaborate effectively with internal stakeholders, cross-functional teams to solve problems, create operational efficiencies, and deliver successfully against high organizational standards.
Major responsibilities:
• Understand the business reality and discover actionable insights from large volumes of data.
• Develop statistical and machine learning models to elevate payroll experience for employees and help payroll operations with day-to-day activities
• Innovate by adapting new modeling techniques and procedures
• Use code (Java, Python, SQL, etc.) to analyze data and build statistical and machine learning models and algorithms.
• Partner with developers and business teams to test your models and solutions in production.
BASIC QUALIFICATIONS
- 1+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 1+ years of data/research scientist, statistician or quantitative analyst in an internet-based company with complex and big data sources experience
PREFERRED QUALIFICATIONS
- Knowledge of statistical packages and business intelligence tools such as SPSS, SAS, S-PLUS, or R
- Experience in at least one of the related science disciplines (optimization - LP, MIP, statistics, machine learning, process control, combinatorial optimization)