Senior Applied Scientist, Amazon Leo Data Science Platform

Amazon

Redmond (WA)

On-site

USD 167,000 - 226,000

Full time

34 hours ago
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Benefits offered by this job

Health insurance
RSUs
Sign-on payments
401(k) matching
Paid time off
Parental leave

Job summary

Amazon Kuiper Manufacturing Enterprises LLC is seeking a Senior Applied Scientist for Project Leo to lead analytics, modeling, and deployment of ML solutions at scale. You will work with cross‑functional teams to address fraud risks, customer experience monitoring, and production readiness.

You will collaborate with data platform and engineering teams to source data, build models, and translate insights into robust, scalable systems that impact customers globally.

Qualifications

  • 3+ years building ML models for business applications.
  • PhD or Master’s with 6+ years of applied research.
  • Experience programming in Java, C++, Python or related language.
  • Experience with neural networks and ML methods.

Responsibilities

  • Identify issues and model performance using analytics.
  • Design and productionalize ML models and rules.
  • Collaborate with stakeholders across teams to deploy solutions.

Skills

ML modeling
Java/C++/Python
Deep learning
Stakeholder comms

Education

PhD or MSc with research exp

Tools

R
scikit-learn
Spark MLlib
MxNet
TensorFlow
NumPy
SciPy

Job description

Description

Amazon Leo is Amazon's low Earth orbit satellite network. Our mission is to deliver fast, reliable internet connectivity to customers beyond the reach of existing networks. From individual households to schools, hospitals, businesses, and government agencies, Amazon Leo will serve people and organizations operating in locations without reliable connectivity.

The Role

As a Senior Applied Scientist in Project Leo, you’ll be leading us in making critical and time sensitive decisions that impact customers. You’ll use your machine learning expertise to build solutions that can scale and solve the business problem, and your engineering experience to build systems that take those solutions to production; it's an exciting opportunity to apply data science to help improve fraud detection accuracy, inference, and customer experience monitoring activity. It’s fast paced, data driven, and impactful.

Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.

Key job responsibilities

The position requires hands‑on expertise in Analytics to identify and isolate issues, Statistical Modeling and traditional Machine Learning, the ability to write queries to aid in data extraction, and the ability to productionalize models. This role is a self sufficient scientist that can source data, build and evaluate models, and ultimately take those models and rules to deployment. You should have excellent communication skills and be able to work with stakeholders at all levels. Above all you should be a passionate, hard‑working and creative person who loves creating business impact, loves solving difficult problems and doesn’t mind getting involved in the details.

A day in the life

As part of the Amazon Leo Data Science Platform team, you will collaborate with a diverse group of internal stakeholders, including fraud operations, Engineering teams, and the Data Platform, to identify and address fraud vulnerabilities. You will have the opportunity to develop rules and ML models to prevent Customer Terminal (CT) usage fraud and abuse. Your role will also allow you to leverage your customer‑obsession skills by thoughtfully considering the user experience and ensuring it is not adversely affected by the mechanisms you design. If you are passionate about working with large‑scale data, we offer ample opportunities to do so.

About The Team

The Amazon Leo Data Science Platform team builds services to ingest, transform, and aggregate data from various devices in Leo Network, and auto detect, diagnose, and resolve issues. We use ML technology to monitor customer experience and prevent fraud and abuse.

Basic Qualifications
  • 3+ years of building machine learning models for business application experience
  • PhD, or Master's degree and 6+ years of applied research experience
  • Experience programming in Java, C++, Python or related language
  • Experience with neural deep learning methods and machine learning
Preferred Qualifications
  • Experience with modeling tools such as R, scikit‑learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
  • Experience with large scale distributed systems such as Hadoop, Spark etc.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

The base salary range for this position is listed below. Your Amazon package will include sign‑on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, WA, Redmond - 167,100.00 - 226,100.00 USD annually

Company - Amazon Kuiper Manufacturing Enterprises LLC

Job ID: A10427350

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