Job Overview

Amazon is hiring an Applied Scientist II for its ReCommerce business in India. This role focuses on applying machine learning to returned products and building models that can improve grading, defect detection, disposition decisions, pricing, and inventory recovery. The position offers an opportunity to work on applied science problems where model performance can directly influence operational efficiency, recovered value, and customer experience.

The Applied Scientist will work across the complete machine learning lifecycle, starting with business problem definition and data preparation and continuing through feature engineering, model training, evaluation, production deployment, monitoring, and retraining. The role combines India-specific model development with adaptation of established Worldwide models to local data, catalog characteristics, languages, and operational conditions.

Key Responsibilities

• Develop machine learning models that help determine whether returned products are suitable for resale or require another recovery path.
• Build computer vision solutions for identifying product defects, assessing physical condition, detecting anomalies, and supporting fraud-related analysis from images.
• Develop models that help route returned products toward appropriate recovery options such as resale, repair, liquidation, donation, or recycling.
• Create pricing and recovery optimization models that consider product grade, condition, and other relevant factors.
• Adapt existing machine learning models for Indian business conditions through retraining, recalibration, evaluation, and validation.
• Analyze differences in data distributions, product catalogs, language characteristics, and operational processes when transferring models between regions.
• Own model development from problem framing and data preparation through feature engineering, training, offline evaluation, online evaluation, deployment, monitoring, and retraining.
• Track model calibration, data drift, performance changes, and business outcomes after deployment.
• Work with software engineers to move models into production environments and establish reliable evaluation processes.
• Partner with product teams to define business opportunities, labels, success metrics, and modeling strategies.
• Collaborate with operations teams to ensure models reflect real-world returns processes and operational requirements.
• Use modern GenAI and LLM tools where appropriate to accelerate research and delivery.

Required Skills

Candidates should have demonstrated experience building machine learning models for business applications. Strong programming ability in at least one relevant language, such as Python, Java, or C++, is required. A solid foundation in algorithms and data structures is also important for developing scalable and reliable analytical solutions.

The role requires experience or strong knowledge in areas such as data mining, numerical optimization, parsing, parallel computing, distributed computing, or high-performance computing. Candidates should be comfortable working with complex datasets and translating business problems into measurable machine learning objectives.

A strong understanding of model evaluation is important because the position involves assessing model performance in production, monitoring calibration and drift, and connecting technical improvements with business metrics. Applicants should also be comfortable working across data preparation, feature engineering, training, evaluation, and deployment activities.

Preferred Skills

Experience with Unix or Linux environments is preferred. Professional software development experience can also be valuable, particularly for scientists working closely with engineering teams to productionize machine learning systems.

Experience with computer vision, optimization, recommendation or decision models, pricing systems, fraud detection, or other applied machine learning applications can be relevant to the responsibilities of this role. Familiarity with GenAI or LLM-based tools is also useful because the team uses modern AI tooling to accelerate research and delivery.

Education

The position requires a PhD, or a Master's degree combined with relevant experience, in Computer Science, Computer Engineering, Machine Learning, or a related technical field. Candidates should also have the required professional or academic background in machine learning and related computational disciplines.

Experience

The role requires at least 3 years of experience building models for business applications. The basic qualifications also specify a PhD, or a Master's degree with 4 or more years of experience in Computer Science, Computer Engineering, Machine Learning, or a related field.

Relevant experience may include machine learning model development, algorithms and data structures, data mining, numerical optimization, parallel and distributed computing, or high-performance computing. Professional software development and Unix/Linux experience are preferred rather than mandatory.

Required Technologies

• Python
• Java
• C++
• Machine Learning
• Computer Vision
• Generative AI
• Large Language Models
• Unix/Linux
• Algorithms and Data Structures
• Data Mining
• Numerical Optimization
• Parallel Computing
• Distributed Computing
• High-Performance Computing
• Model Evaluation
• Feature Engineering
• ML Model Monitoring

Soft Skills

This position requires strong analytical thinking and the ability to move between scientific research, engineering implementation, and business objectives. Applied Scientists should be comfortable framing ambiguous problems, testing hypotheses, interpreting results, and making decisions based on evidence.

Cross-functional collaboration is also central to the role. The scientist will work with engineering, product, operations, and other science teams, so clear technical communication and the ability to explain modeling decisions to different audiences are important. A high level of ownership is useful because the position covers the full lifecycle of machine learning solutions.

Benefits of Working in this Role

This role provides the opportunity to work on applied machine learning problems connected to a large-scale ReCommerce operation. Scientists can see how models influence practical areas such as product grading, defect detection, inventory routing, pricing, and recovery optimization.

The position also offers exposure to both India-first model development and adaptation of Worldwide machine learning solutions. This combination can provide valuable experience in model transfer, recalibration, distribution shifts, production monitoring, and business-focused machine learning evaluation.

Work Mode

The provided job description does not specify an onsite, hybrid, or remote work arrangement. Candidates should confirm the applicable work model with Amazon during the recruitment process.

Location

This Applied Scientist II opportunity is associated with Amazon ReCommerce India and is listed for the Bengaluru, Karnataka, India location. It may be relevant to professionals searching for Amazon jobs in Bengaluru, Applied Scientist jobs, Machine Learning jobs, AI jobs, Computer Vision jobs, and experienced Data Science and Software Jobs in India.

Who Should Apply

This role is suitable for experienced machine learning professionals who have built models for real business applications and are comfortable taking ownership from problem definition through production. Candidates with backgrounds in machine learning, computer vision, algorithms, optimization, data mining, distributed computing, or high-performance computing should consider the opportunity.

Applicants should be prepared to demonstrate programming ability in Python, Java, C++, or a related language. Experience with Linux or Unix and professional software development can strengthen an application. Candidates with practical experience connecting machine learning results to measurable business outcomes may be particularly well aligned with the role.

Career Growth

Working across applied science, machine learning engineering, computer vision, optimization, model evaluation, and production deployment can help professionals build a broad technical profile. The role also provides exposure to large-scale operational problems and cross-functional collaboration with engineering, product, operations, and science teams.

Application Advice

Before applying, make sure your resume clearly explains the machine learning models you have built and the business problems they addressed. Highlight your programming languages, algorithms experience, data mining or optimization work, and any production machine learning projects.

If you have worked with computer vision, model monitoring, calibration, drift detection, pricing optimization, fraud detection, recommendation systems, distributed computing, or GenAI tools, describe the work with specific technical context. Also emphasize your experience collaborating with engineering and product teams and taking models from experimentation toward production.