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I have been working in the data science and operations research industry for a while now. Since about 2.5 years, I work in the IT department of a startup and my role is fully focussed on putting operations research in practice. Over the course of these years, my business title has changed from "Operations Researcher" to "Operations Research Scientist" to "Operations Research Engineer". In the end, your work is of course not about the title, but about the content. In our company, the OR profile is considered as a mix of software engineer and data scientist.

I recently reflected on this, and was wondering how one would distinguish between operations research engineer / scientist / analyst / ... outside of my bubble. Hence the question: how would you see the differences between these roles, and are there even any?

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I see no difference between these titles and it's becoming increasingly risky to assume anything based on someone's job title. I've met plenty of "managers" where the only thing they manage is their inbox.

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As Riley said, the job title is not a good indicator, you will need to check the job description. But, as a rule of thumb, an analyst is someone expected to do models and analysis of results, but not to manage people, implement solutions in software, etc. A scientist is more related to modeling and an engineer with the software implementation of the model.

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"... how one would distinguish between operations research engineer / scientist / analyst / ... outside of my bubble. Hence the question: how would you see the differences between these roles, and are there even any?".

Two ways to distinguish between the naming of the position is by the name of the course in a post-secondary institutions and at an employer.

  • Employers who ask for qualifications that are not taught in school are also seldom willing to pay for them.

  • Positions not correctly named are usually open to great variance as to what is entailed, and resulting pay.

  • It's best to either establish in writing what is required or avoid employers who don't know what they want and the usual amount to be paid.

The name of the position, pay, responsibilities and years in school required are one set of distinguishing requirements, another way to look at it is from the viewpoint of supply and demand.

  • You wouldn't want to spend many years in school studying extremely technical material to do a job that has many responsibilities, paid little, and was difficult to find.
  • I guess it's ideal if the study isn't too lengthy or mindbending, few people take the course, the jobs are plentiful, and the responsibilities are enough to provide some challenges, but it pays well.

One source of job descriptions and average pay is online job sites, for example: GlassDoor and BetterTeam - Operations Research Analyst Job Description Template.

  • Operations Research Scientist Salaries in Canada: \$79,471/yr (Average Base Pay, out of 1,414 salaries)

  • Operations Research Engineer Canada \$71,590

  • Operations Research Analyst Canada \$58,001

When you look up the average pay in the USA it's much higher, worth moving. Source: ZipRecruiter:

  • Operations Research Analyst USA \$88,674/yr
  • Operations Research Engineer USA \$162,999/yr

A less biased source of information is probably the US Bureau of Labor Statistics. Clicking on related jobs (with lesser requirements and greater pay) we find: Computer and Information Research Scientists - Masters, \$131,490/yr or Data Scientist - Bachelor, \$100,910/yr.

Future outlook is another consideration, while not related to your question it's useful to consider the future before undertaking the course or planning an employment career.

Some careers are quite safe as far as longevity is concerned, others are either being phased out or have such rapidly changing requirements that your paper is outdated before the ink can dry.

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Broadly OR:
Analyst - generally quant analyst building models and conducting data collection, cleaning, exploratory analysis & building dashboards.

Scientist - building new algorithms and writing model prototypes. Generally PhD is needed. Expectation may include to build propriety solvers.

Engineer - Industrial Engineer role designing models with known algorithms/solvers & implementing on shop floor. Sometimes could be same as OR Analyst.

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