Data Scientist II, AB Ops Analytics, Amazon

Job Description

Amazon is hiring a Data Scientist II, AB Ops Analytics, to develop scalable analytical solutions for business decision-making. The role involves analysing complex datasets, building and validating statistical and machine learning models, automating operational processes, and identifying business opportunities. The position requires collaboration with data, engineering, and business teams to improve products, customer experiences, and operational efficiency. 

Qualification: Master’s degree in science 

Experience: 2+ years 

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Responsibilities

• Apply data science techniques to solve complex customer and business problems using specialised analytical approaches.
• Discover opportunities and create solutions through process automation and the optimisation of internal and external products.
• Analyse data, models, patterns, and anomalies in order to pinpoint operational issues and suggest corrective measures.
• Create and test statistical, mathematical, econometric, network, and machine learning models for business applications.
• Work together with engineering teams in order to implement models that will enhance customers’ experience and business operations.

Requirements

• At least two years of relevant professional experience in data science tackling business/operational issues effectively.
• Three years of experience working with SQL, Python, R, SAS, MATLAB, or statistical technology in practical application.
• Three years of experience with machine learning, statistics, data analysis, and performance evaluation.
• Experience in guiding researchers, assessing artificial intelligence systems, and participating in research or teaching material effectively.
• A master’s degree in a STEM field or equivalent experience in the application of theoretical models in real-world business scenarios.

Preferred Qualifications

• A master’s degree in science, technology, engineering, or mathematics is a desirable qualification for advanced analytics roles and research.
• Experience in the application of machine learning principles in reasoning, optimisation, and advanced problem-solving.
• Experience in using Python, Perl, or other programming languages in efficient analytics and modelling processes.
• Experience in setting benchmarks for evaluating the performance of generative AI models and analysing results.
• Experience in working on cross-disciplinary projects, quantitative analysis, business decision-making, and conveying complex ideas via different mediums.