Amazon Hiring Data Scientist II in India | Risk & Compliance

Job Overview

Amazon is hiring a Data Scientist II for its Risk and Compliance Solutions organization in India. This role focuses on using predictive analytics, machine learning, and data science to help identify compliance risks, understand control effectiveness, and support informed decisions across regulated business areas.

The position involves solving complex analytical problems where defining the right target variable can be as important as selecting the model. You will work with Compliance and first line of defense partners to frame business questions, develop analysis-ready datasets, build and calibrate predictive models, and move analytical results into production reporting and intelligence workflows.

The role is especially relevant for data scientists who enjoy applying statistical and machine learning methods to real-world risk problems. You will also work with large technology datasets and collaborate with data engineers, business intelligence engineers, software development engineers, and business stakeholders.

Key Responsibilities

• Build, deploy, monitor, and calibrate predictive models that assess whether open risks and control weaknesses may develop into compliance breaches.
• Work with Compliance and first line of defense partners to define meaningful target variables and labels that align with established risk taxonomies.
• Combine control testing information, alerts, escalations, investigation outcomes, and other business data into reliable datasets for analysis and modeling.
• Evaluate control effectiveness across regions while distinguishing actual control performance issues from changes in underlying risk exposure.
• Develop historical backtesting approaches and choose evaluation methods appropriate for rare outcomes, including precision-recall and model calibration measures.
• Contribute analytical methods for testing monitoring rules and risk models before changes are released.
• Translate model results into practical intelligence for Compliance Officers and business teams through reporting, briefings, forums, and alerting.
• Identify suitable opportunities to use generative AI to reduce manual effort in control testing, requirements translation, and reporting.
• Explain model assumptions, results, limitations, and methodology to technical and non-technical stakeholders.
• Document analytical methods to support internal audit and external examination requirements.
• Develop reusable approaches that can be extended from one compliance domain to other areas within the Risk and Compliance Solutions portfolio.
• Learn and use relevant Amazon data resources to select appropriate information for analytical and modeling needs.

Required Skills

The role requires at least two years of data scientist experience. Candidates should also have at least three years of experience using data querying languages such as SQL, scripting languages such as Python, or statistical and mathematical software such as R, SAS, or MATLAB.

Strong knowledge of machine learning and statistical modeling is required, including practical experience analyzing model performance and understanding parameters that influence results. The position also calls for experience applying theoretical models in practical environments.

Candidates should have at least one year of experience guiding or coaching a group of researchers, working with or evaluating AI systems, and creating or contributing to mathematical textbooks, research papers, or educational content.

Preferred Skills

A Ph.D. in a STEM discipline is preferred. Additional experience with Python, Perl, or another scripting language can strengthen a candidate's profile.

Experience in a machine learning or data science position at a large technology company is preferred. Candidates who have created benchmarks for evaluating generative AI model performance may also be well suited to the role.

Experience working across multiple teams and disciplines is valuable, particularly when analytical work involves technical, compliance, and business stakeholders. Strong quantitative reasoning and the ability to convert data into business recommendations are also preferred.

Education

A Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in a STEM field, is specified in the basic qualifications. A Ph.D. in STEM is listed as a preferred qualification.

Experience

The minimum stated experience includes two years of data scientist experience, three years using SQL, Python, R, SAS, MATLAB, or similar data and statistical technologies, and three years working with machine learning or statistical modeling techniques.

The role also requires at least one year of experience guiding or coaching researchers, evaluating or working with AI systems, and contributing to mathematical or educational research content. Candidates should be able to demonstrate practical application of theoretical models.

Required Technologies

Key technologies and analytical areas include SQL, Python, R, SAS, MATLAB, machine learning, statistical modeling, generative AI, AI systems, predictive modeling, Amazon Redshift, Amazon SageMaker, AWS Lambda, and Amazon QuickSight.

The role also involves techniques such as precision-recall evaluation, calibration, backtesting, quantitative analysis, and data modeling. These capabilities support predictive risk analysis, reporting, and production intelligence.

Soft Skills

Clear communication is essential because the Data Scientist will present technical findings and model limitations to Compliance Officers, business stakeholders, and other partners. The ability to explain complex concepts in practical language is important when analytical results influence risk and compliance decisions.

Strong problem-solving, critical thinking, collaboration, and analytical judgment are also important. Candidates should be comfortable working across disciplines, challenging assumptions, documenting methodology, and making evidence-based recommendations.

Benefits of Working in this Role

This position offers the opportunity to apply data science to complex compliance and risk problems within a large technology environment. It combines machine learning, statistical analysis, predictive modeling, generative AI, data engineering collaboration, and business decision support.

The work can provide valuable experience in building models for rare outcomes, evaluating control effectiveness, communicating model results to governance-focused audiences, and developing reusable analytical capabilities across multiple domains.

Work Mode

The provided job description does not specify whether the position is onsite, hybrid, or remote. The role is listed for Amazon Development Centre (India) Private Limited, and no specific city is provided.

Location

The job description identifies India as the relevant location through the employer entity but does not provide a specific city. This listing therefore records the location as India without inventing a city.

Who Should Apply

This opportunity is suitable for experienced Data Scientists with backgrounds in machine learning, statistical modeling, predictive analytics, and applied quantitative research. Candidates should be comfortable working with structured data, defining analytical targets, evaluating model performance, and translating technical findings into business decisions.

Professionals with experience in risk analytics, compliance analytics, financial crime-related modeling, AI evaluation, or large-scale machine learning environments may find their background relevant.

Career Growth

Experience in this role can strengthen expertise in applied machine learning, predictive risk modeling, model evaluation, generative AI, quantitative analysis, and data-driven governance. It can support future growth into senior data science, machine learning engineering, applied science, risk analytics, AI evaluation, or technical leadership roles.

Application Advice

Tailor your resume to show measurable experience in data science and machine learning rather than listing tools alone. Highlight projects where you defined targets, built predictive models, performed backtesting, evaluated rare-event models, or translated analytical findings into business decisions.

Clearly mention your experience with SQL, Python, R, SAS, MATLAB, or other relevant analytical technologies. If you have worked with Amazon Redshift, SageMaker, AWS Lambda, QuickSight, GenAI evaluation, or large-scale ML systems, make those contributions easy to identify.

Also emphasize research or educational content experience, researcher mentoring, cross-functional collaboration, and your ability to explain complex analytical concepts to non-technical stakeholders.