Sr. Applied Scientist, Last Mile Science

Amazon is hiring a Sr. Applied Scientist, Last Mile Science in Bengaluru, India. Level rates it ; you can apply on Amazon.

Level's read on this job

On Level's scale, this applied scientist role is Builds AI ●●●●: the job is building AI systems.
The posting mentions machine learning, operations research and statistics among its tools and skills.
Level classes it as a senior role.
Amazon has 20,000+ other open roles on Level.

AI in this role

Develop machine learning models and optimization algorithms to improve last-mile logistics and delivery operations.

machine-learningoperations-researchstatisticspredictive-modeling

Have you ever ordered a product on Amazon and when that box with the smile arrived you wondered how it got to you so fast? Have you wondered where it came from and how much it cost Amazon to deliver it to you? If so, the WW Amazon Logistics, Business Analytics team is for you. We manage the delivery of tens of millions of products every week to Amazon’s customers, achieving on-time delivery in a cost-effective manner.

We are looking for an enthusiastic, customer obsessed, Sr. Applied Scientist with good analytical skills to help manage projects and operations, implement scheduling solutions, improve metrics, and develop scalable processes and tools. The primary role of an Operations Research Scientist within Amazon is to address business challenges through building a compelling case, and using data to influence change across the organization. This individual will be given responsibility on their first day to own those business challenges and the autonomy to think strategically and make data driven decisions. Decisions and tools made in this role will have significant impact to the customer experience, as it will have a major impact on how the final phase of delivery is done at Amazon.

Ideal candidates will be a high potential, strategic and analytic graduate with a PhD in (Operations Research, Statistics, Engineering, and Supply Chain) ready for challenging opportunities in the core of our world class operations space. Great candidates have a history of operations research, and the ability to use data and research to make changes.

This role requires robust program management skills and research science skills in order to act on research outcomes. This individual will need to be able to work with a team, but also be comfortable making decisions independently, in what is often times an ambiguous environment.

Responsibilities may include:
- Develop input and assumptions based preexisting models to estimate the costs and savings opportunities associated with varying levels of network growth and operations
- Creating metrics to measure business performance, identify root causes and trends, and prescribe action plans
- Managing multiple projects simultaneously
- Working with technology teams and product managers to develop new tools and systems to support the growth of the business
- Communicating with and supporting various internal stakeholders and external audiences

Basic qualifications

- 5+ years of building machine learning models or developing algorithms for business application experience
- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field, or Master's degree and 5+ years of building machine learning models for business application experience
- Knowledge of programming languages such as C/C++, Python, Java or Perl
- Experience with neural deep learning methods and machine learning

Preferred qualifications

- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field

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.

How we rate this

Sr. Applied Scientist, Last Mile Science at Amazon rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Sr. Applied Scientist, Last Mile Science is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in IN, KA, Bangalore.
The job was posted on September 17, 2026.
Seniority: senior.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Machine learningOperations ResearchStatisticsPredictive Modeling

Also common in applied scientist jobs at this level

LLM Generative AI NLP Reinforcement learning TensorFlow Spark

Pay

Typical applied scientist job: $167k

This posting doesn't list pay. Use the typical applied scientist figure when pay comes up. See applied scientist jobs

Questions you could be asked

Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where operations research was part of your work. What did you do?
Tell me about a project where statistics was part of your work. What did you do?
Tell me about a project where predictive modeling was part of your work. What did you do?
How would you decide a model or AI system is ready to ship?

Adapt your resume

List these exact terms on your resume: Machine learning, Operations Research, Statistics, and Predictive Modeling. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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