Amazon

Data Scientist II

Posted On

March 9, 2026

Location

Bengaluru, Karnataka

Experience Level

Freshers/Experienced

Job Type

Data Science

Apply By

March 31, 2026

Job Description

AWS Infrastructure Services owns the design, planning, delivery, and operation of all AWS global infrastructure. In other words, we’re the people who keep the cloud running. We support all AWS data centers and all of the servers, storage, networking, power, and cooling equipment that ensure our customers have continual access to the innovation they rely on. We work on the most challenging problems, with thousands of variables impacting the supply chain — and we’re looking for talented people who want to help.

You’ll join a diverse team of software, hardware, and network engineers, supply chain specialists, security experts, operations managers, and other vital roles. You’ll collaborate with people across AWS to help us deliver the highest standards for safety and security while providing seemingly infinite capacity at the lowest possible cost for our customers. And you’ll experience an inclusive culture that welcomes bold ideas and empowers you to own them to completion.

Do you love problem solving? Are you looking for real world Supply Chain challenges? Do you have a desire to make a major contribution to the future, in the rapid growth environment of Cloud Computing?

Amazon Web Services is looking for a highly motivated, Data Scientist to help build scalable, predictive and prescriptive business analytics solutions that supports AWS Supply Chain and Procurement organization. You will be part of the Supply Chain Analytics team working with Global Stakeholders, Data Engineers, Business Intelligence Engineers and Business Analysts to achieve our goals.

We are seeking an innovative and technically strong data scientist with a background in optimization, machine learning, and statistical modeling/analysis. This role requires a team member to have strong quantitative modeling skills and the ability to apply optimization/statistical/machine learning methods to complex decision-making problems, with data coming from various data sources. The candidate should have strong communication skills, be able to work closely with stakeholders and translate data-driven findings into actionable insights. The successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and ability to work in a fast-paced and ever-changing environment.

Key job responsibilities
- Demonstrate thorough technical knowledge on feature engineering of massive datasets, effective exploratory data analysis, and model building using industry standard time Series Forecasting techniques like ARIMA, ARIMAX, Holt Winter and formulate ensemble model.
- Design and deploy AI agents for supply chain optimization. Build evaluation frameworks with guardrails and benchmarks ensuring reliability. Own the full development lifecycle adhering to responsible AI principles. Drive the AI roadmap leveraging emerging agentic AI trends.
- Proficiency in both Supervised(Linear/Logistic Regression) and UnSupervised algorithms(k means clustering, Principle Component Analysis, Market Basket analysis).
- Experience in solving optimization problems like inventory and network optimization . Should have hands on experience in Linear Programming.
- Work closely with internal stakeholders like the business teams, engineering teams and partner teams and align them with respect to your focus area

- Detail-oriented and must have an aptitude for solving unstructured problems. You should work in a self-directed environment, own tasks and drive them to completion.
- Excellent business and communication skills to be able to work with business owners to develop and define key business questions and to build data sets that answer those questions
- Work with distributed machine learning and statistical algorithms to harness enormous volumes of data at scale to serve our customers

About the team
Diverse Experiences
Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.

Why AWS
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.

Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.

Inclusive Team Culture
AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do.

Mentorship and Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.- 5+ years of data scientist experience
- 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
- Experience leading the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems, or experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware

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