Experience with Python and basic knowledge of data science is required. We benchmark AI products. During these benchmarks, analysts may need to design holdout datasets, setup open source software, call APIs, record and analyze results.

Professional level of English: AI analysts need to be able to read and understand latest papers on AI solutions.

Applicants need at most 3 years of work experience. This is an entry level role. Fresh graduates can apply.

This is a full-time role, it is not suitable if you are completing a bachelor's or more advanced degree.

About AIMultiple

AIMultiple, the data-driven enterprise AI analyst, receives hundreds of thousands of business visits per month (source: https://www.similarweb.com/website/aimultiple.com). 60% of US Fortune 500 rely on AIMultiple every month.

Hundreds of technology vendors from the US, Europe, and Asia sponsor AIMultiple articles and vendor lists to inform AIMultiple users about their solutions.

Established in 2017, AIMultiple is incorporated in Estonia and Singapore.

AIMultiple is remote-only and will remain without an office.

Feel free to learn more about us:

• https://aimultiple.com/culture
• https://aimultiple.com/about

Role:

The AI analyst will work with the AIMultiple team including industry analysts, data engineers, software engineers, and designers. The team has a range of experience from fresh grads to those with decades of experience including experience at firms like McKinsey & Company.

The AI analyst will be responsible for:

• Using emerging AI products (e.g. AI agents in mobile devices) to understand their advantages and disadvantages
• Choosing open-source datasets or developing proprietary datasets for benchmarking
• Designing benchmarks with reproducible results and clear, practical methodology
• Running benchmarks

Benchmarks completed by the AI analyst will inform the purchasing decisions of millions of corporations every year.

Requirements:

• Willingness to have a career as an AI analyst. This is a role that combines qualitative, quantitative and programming skills.
• Intellectual curiosity and eagerness to learn new technology use cases such as synthetic data and answer questions such as “How can we benchmark the performance of different synthetic data solutions?”
• Ability to follow the scientific principles of honesty, openness and falsifiability while comparing different solutions and evaluating competing claims.

Hiring process:

A growing body of research shows that interview performance is not correlated with job performance. Therefore, our hiring performance will be purely based on 2 take home assessments.

Compensation:

Includes a fixed component and a strong, objectively-calculated performance based component.