Job Description
AOP(Analytics Operations and Programs) team is responsible for creating core analytics, insight generation and science capabilities for ROW Ops. We develop scalable analytics applications and research modeling to optimize operation processes.. You will work with professional Product Managers, Data Engineers, Data Scientists, Research Scientists, Applied Scientists and Business Intelligence Engineers using rigorous quantitative approaches to ensure high quality data/science products for our customers around the world.
We are looking for an Applied Scientist to join our growing Science Team in Bangalore/Hyderabad. As an Applied Scientist, you are able to use a range of science methodologies to solve challenging business problems when the solution is unclear. You will be responsible for building ML models to solve complex business problems and test them in production environment. The scope of role includes defining the charter for the project and proposing solutions which align with org's priorities and production constraints but still create impact . You will achieve this by leveraging strong leadership and communication skills, data science skills and by acquiring domain knowledge pertaining to the delivery operations systems. You will provide ML thought leadership to technical and business leaders, and possess ability to think strategically about business, product, and technical challenges. You will also be expected to contribute to the science community by participating in science reviews and publishing in internal or external ML conferences.
Our team solves a broad range of problems that can be scaled across ROW (Rest of the World including countries like India, Australia, Singapore, MENA and LATAM). Here is a glimpse of the problems that this team deals with on a regular basis:
• Using live package and truck signals to adjust truck capacities in real-time
• HOTW models for Last Mile Channel Allocation
• Using LLMs to automate analytical processes and insight generation
• Using ML to predict parameters which affect truck scheduling
• Working with global science teams to predict Shipments Per Route for $MM savings
• Deep Learning models to classify addresses based on various attributes
Key job responsibilities
1. 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
2. Design, develop, evaluate and deploy, innovative and highly scalable ML models
3. Work closely with other science and engineering teams to drive real-time model implementations
4. Work closely with Ops/Product partners to identify problems and propose machine learning solutions
5. Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model maintenance
6. Work proactively with engineering teams and product managers to evangelize new algorithms and drive the implementation of large-scale complex ML models in production
7. Leading projects and mentoring other scientists, engineers in the use of ML techniques
As part of our team, candidate in this role will work in close collaboration with other applied scientists and cross functional teams on high visibility projects with direct exposure to the senior leadership team on regular basis.
About the team
This team is responsible for applying science based algo and techniques to solve the problems in operation and supply chain. Some of these problems include Truck Scheduling, LM capacity planning, LLM and so on.
- 4+ years of building models for business application experience
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- 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
- Experience using Unix/Linux
- PhD in CS, EE with specialisation in ML
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