📈 #66 Operations Research careers: paths, skills, and PhD considerations
Insights on education, career trajectories, and skill building for success
I receive repetitive questions occasionally.
And I enjoy answering your questions via LinkedIn DMs or email.
But it’s better to have a common place to craft a better answer than just giving the same advice to everyone.
Today in Feasible, I want to answer the 3 most common questions I get:
🎓 Do you need a PhD for a career in OR?
🛤️ What career paths exist in the industry?
💡 What skills should you develop for the industry?
🎧 At the top, you can listen to a podcast-like version of this post!
If you’re ready… Let’s dive in! 🪂
🎓 Do you need a PhD for a career in OR?
Short answer: no.
But you don’t come to Feasible for brief answers.
Long answer: it depends.
Let’s evaluate the advantages and disadvantages of doing a PhD.
✅ Pros of a PhD in OR
You’ll get a solid foundation in advanced topics. My PhD focused on heuristic strategies to solve warehouse optimization problems, and I became proficient in writing algorithms from scratch. There’s a wide range of OR topics, so you’ll use specific techniques depending on your project with your supervisors.
To access a research position, like being an academic researcher or a specialized industry role, you need a PhD, or it is significantly more difficult.
In the industry, while a PhD is not specifically needed, Unlock your Operations Research career path showed it is preferred over a Master’s. That doesn’t mean you need it, but the market expects it.
❌ Cons of a PhD in OR
It will take time. For me, it was 3 years and 4 months (including one year overlapping with an industry job), but it’s common to spend additional time.
If you want to enter the industry, decide quickly. If you do a PhD and want to stay in academia, it becomes harder to get an industry position. The more time you invest in academia, the less time you spend on skills like software engineering or business acumen, which are valuable in the industry.
Many companies value good enough solutions over complicated models that get the exact solution. You’ll enjoy your acquired knowledge in academia just for the sake of it (as I do), but not because it provides an advantage to solve business problems.
Should you pursue a PhD?
The main question I receive is that one, and I answer with additional questions to help them decide.
What is your primary passion?
Do you excel in deep theoretical research, or do you prefer applying practical solutions in a fast-paced business environment?
What are your professional aspirations?
Are you aiming for an academic or research career, or looking to contribute to industry projects?
How much time are you willing to commit?
A PhD can take 4-6 years. Consider if you’re ready for that investment relative to your career timeline.
In general, a PhD is very rewarding. I enjoyed it a lot and can’t express how much I enjoyed it. But I didn’t want to stay in academia, so I decided to go to the industry right after the project’s budget I was in was consumed.
If you can’t decide, consider doing a Master’s degree and ask yourself these 3 questions.
The question is… What career paths can you apply to in the industry?
🛤️ What career paths exist in the industry?
We can find roles ranging from consultant to research scientist, depending on the technical depth required for a position.
I’ll cover the three most prominent career paths and give you an overview of the industry roles.
Let’s cover them from least to most technical expertise required.
Operations Research Consultant
🔬 Focus: solve business problems by collaborating with external clients.
💡 They excel in communication, project management, and stakeholder engagement, while needing technical skills like modeling, data analysis, and translating complex models into actionable business insights.
Business acumen and communication are essential for a successful career in this role.
⚡ Main challenges: balancing technical rigor with practical business knowledge.
💸 According to Glassdoor, an OR consultant’s average base pay in the U.S. is around $95,000 per year. Entry-level consultants start around $70k-$80k.
With a few years of experience or an advanced degree, mid-level salaries can approach six figures. Senior OR consultants or managers (especially in high cost-of-living cities or top consultancies) can exceed $120k–$130k.
Operations Research Engineer
🔬 Focus: implementing and deploying OR models in real-world systems.
💡 They excel in problem structuring, teamwork, and cross-functional collaboration. They know how to integrate an optimization engine with production systems, technically.
Strong computational skills are essential here.
⚡ Main challenges: ensuring scalability and practical deployment of models.
💸 Their pay can be high in tech hubs. Glassdoor reports a median base salary of $132,500 (total pay near $195k) for OR Engineer roles in the U.S.
Entry-level OR engineers (with a BS/MS) see $80k–$100k offers in the U.S. Those with several years of experience or senior titles can hit $130k-$150k+ in Silicon Valley, New York, etc.
Operations Research Scientist
🔬 Focus: research, algorithm development, and exploring new optimization methods.
💡 They excel in advanced mathematics, theoretical modeling, and complex algorithms.
⚡ Main challenges: translating advanced models into practical applications.
💸 In the U.S., many of these positions require advanced degrees (MS/PhD) and six-figure pay. Glassdoor estimates an average base salary of $125,000 (total compensation ~$170k including bonuses/equity) for OR Scientists in 2025.
Entry-level OR scientists (e.g. new PhDs) often start in the low-to-mid six figures, while experienced ones in top firms can earn above $150k.
Take these salaries with caution, as they vary globally.
North American roles pay the highest, followed by Western Europe and emerging markets (adjusted for cost of living). Salaries align with local pay scales.
OR professionals are well-compensated compared to other fields, and specialized expertise commands high pay.
Expect significant salary growth with experience.
As you consider these career paths in OR, reflect on the choice to pursue a PhD.
The required technical depth and advanced skills will align with your chosen path.
Understanding these options can help you make a more informed decision about your educational and professional path.
💡 What skills should you develop for the industry?
I also receive this interesting question.
I always refer to my previous Feasible banner. Remember it?
An OR practitioner at a logistics company codes in Python and integrates optimization models into cloud systems while collaborating closely with product teams and stakeholders to ensure solutions meet business needs.
There are 3 major areas to develop. Let’s break that down.
📐 Operations Research knowledge
“codes in Python”
You can’t be called an OR practitioner without OR-related knowledge, but I’ll go to the fundamentals.
I like to apply the Pareto rule in my life, and this is no exception. What should I focus on to get the most value?
There are two main abilities to develop:
The ability to create mathematical models.
The ability to write algorithms to address problems.
The first one will help you remove natural language ambiguity, translate real-life business problems into math, and write models for a solver. Focus on MILPs (Mixed Integer Linear Programming) for starters, then you can explore further. Use open-source generic-purpose tools like pyomo, PuLP, or Google OR-Tools.
The second one will help you quickly solve optimization problems or get initial solutions. It will develop your creativity for solving problems and spark your interest in exploiting problem structure. Focus on construction heuristics, local search methods, and learn one or two metaheuristics (you know the one I love most). Start from scratch and don’t rely on frameworks like JAMES or optsicom until you master the foundations.
Those skills are essential for theoretical research and practical model development.
🧑🏻💻 Programming
“integrates optimization models into cloud systems”
You need at least two main things: it doesn’t need to be in the cloud, but you need to offer a way to consume your optimization engine:
Good coding skills.
Knowledge to create a server or API to serve the model.
What does that mean?
Good coding skills go beyond writing working code; they encompass technical proficiency, problem-solving ability, and best practices. Key aspects include code readability and maintainability, efficiency, using version control systems like Git, error handling and robustness, writing tests, and writing modular code following design patterns.
There’s a course on Software Design and Architecture from the University of Alberta on Coursera.
If you’re going to serve your model as an API, which is common to connect it to other parts of a bigger product, you need to create a server to host the model (e.g., with Flask or FastAPI), connect to databases to read input information and write the model’s solution, and have knowledge of deployment and minimal infrastructure.
Since Python is dominant, it’s easy to develop everything in Python. If you’re in a team with Data Scientists or ML Engineers, integration into the system will be simpler.
📈 Business acumen
“collaborating closely with product teams and stakeholders to ensure the solutions meet business needs”
I highlight one technical skill here, but most are not technical.
Since you’ll engage stakeholders from the project’s start, offer them a way to see the optimization problem solutions. Let them parametrize the model and run it independently. You can use Streamlit for this during development.
The main critical ability is to define and model real-world problems to foster communication. It is essential to explain complex concepts in simple terms.
Another highlight is that you may not have specific industry knowledge, but exposing yourself to different problems will develop your creativity and help you understand the business’s details and solve their challenges.
As Operations Research is evolving rapidly, it's essential to continuously develop skills.
A growth mindset is crucial for long-term success in this dynamic field, whether you are staying updated on the latest mathematical modeling techniques, enhancing your programming skills, or deepening your industry knowledge.
🏁 Conclusions
We’ve covered a lot today.
I hope this post clarifies your ideas about:
Pursuing a PhD? Follow your passion.
Industry career paths and salaries.
The skills needed to succeed in the industry are divided into three main skills an OR practitioner should develop.
Operations Research, where you solve complex problems, learn new skills daily, and receive a compensation for it!
If you have questions about careers in Operations Research or want to discuss your goals and how to achieve them, I am happy to connect. Reach out anytime.
Let’s keep optimizing,
Borja.
Very good and accurate!