21 Applied Science Jobs in Seattle, WA
Sorry! This job is no longer available. Please explore similar jobs listed on the left.
Amazon is Hiring an Applied Science Near Seattle, WA
DESCRIPTION
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud?
Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems?
Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment?
If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day.
Key job responsibilities
Use machine learning and statistical techniques to create scalable risk management systems
Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends
Design, development and evaluation of highly innovative models for risk management
Working closely with software engineering teams to drive real-time model implementations and new feature creations
Working closely with operations staff to optimize risk management operations,
Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation
Tracking general business activity and providing clear, compelling management reporting on a regular basis
Research and implement novel machine learning and statistical approaches
We are open to hiring candidates to work out of one of the following locations:
Seattle, WA, USA
BASIC QUALIFICATIONS
- Master's degree in computer science, mathematics, statistics, machine learning or equivalent quantitative field
- Experience programming in Java, C , Python or related language
- Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse
PREFERRED QUALIFICATIONS
- A PhD in CS, Machine Learning, Statistics, Operations Research or relevant field
- 3 years of industry experience in predictive modeling and analysis
- Strong Machine Learning breadth and depth
- Strong skills with SQL Strong skills with Spark/Python/Perl (or similar)
- Ability to think creatively and solve problems
- Good written and spoken communication skills
- Demonstrated track record of cultivating strong working relationships and driving collaboration across multiple technical and business teams
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $115,100/year in our lowest geographic market up to $212,800/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.
Job Summary
Full Time
Transportation
$137k-171k (estimate)
05/11/2024
05/23/2024
RANDALLSTOWN, MD
25 - 50
2018
$50M - $200M
Transportation
