Applied Scientist II, International Machine Learning
Teknik, data och digitalt · Data, AI och analys · Maskininlärning · Datavetenskap
I korthet
This role involves developing and deploying advanced machine learning solutions to solve business problems for Amazon India Consumer Businesses. You will analyze large datasets, build and validate scalable ML models, and collaborate with engineering and business teams to drive impactful results.
Ansvarsområden
- Use machine learning and analytical techniques to create scalable solutions for business problems.
- Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes.
- Design, develop, evaluate and deploy, innovative and highly scalable ML models.
- Work closely with software engineering teams to drive real-time model implementations.
- Work closely with business partners to identify problems and propose machine learning solutions.
- Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model maintenance.
- Work proactively with engineering teams and product managers to evangelize new algorithms and drive the implementation of large-scale complex ML models in production.
- Lead projects and mentor other scientists, engineers in the use of ML techniques.
Krav
- 3+ years of building models for business application experience.
- PhD, or Master's degree.
- Experience in patents or publications at top-tier peer-reviewed conferences or journals.
- Experience programming in Java, C++, Python or related language.
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing.
Önskade kvalifikationer
- Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning.
- Experience applying theoretical models in an applied environment.
#machine learning#data science#big data#quantitative sciences#recommendation engines#fraud detection#optimization#risk models#product attribute extraction#customer suggestions#algorithm development#model implementation#model validation#model maintenance#deep learning