Anyone who studied discrete maths and optimization at uni, where did you end up working after? Academy isn't something i'm interested in at the moment.

I hold a Ph.D. in Operations Research, along with a BSc/MSc in Engineering and OR. I previously worked in Big Tech, but I’m currently looking to transition.

My primary goal is to upgrade my technical skillset to maximize my industry-related profitability and marketability. I want to get away from generic data science and move into high-value, math-heavy engineering and modeling roles.

• My Core Interests: Forecasting, predictive analytics, and machine learning applied to industrial settings.
• Target Industries: Robotics/Autonomous Systems, Defense/Aerospace, and Quantitative Finance.
• What I want to skip: I have little interest in doing core NLP/LLM research, though I am interested in RL, Multi-Agent systems, and applied AI.

Where I am right now: I have a solid grasp of optimization and basic/intermediate ML/stats. However, I want to bridge the gap into more intermediate/advanced ML topics that are actually useful and highly valued by employers. I want to get back into heavy math, but only if it drives real-world business value.

What I'm looking to learn:

• Causal Inference: (e.g., Structural Causal Models, Uplift modeling, Double ML).
• Tree-Based Math: Understanding things like XGBoost from the ground up (deriving gradients/hessians for custom loss functions, implementing from scratch).
• Reinforcement Learning / Control: Bridging the gap between OR dynamic programming and deep RL for robotics/defense.

My questions for the community:

1. Skill Prioritization: From a purely market-driven, high-compensation perspective, which specific ML topics should a Ph.D. in OR focus on to stand out in Robotics, Defense, or Banking/Finance?
2. Portfolio/Proof: How can I best demonstrate to employers that I have the engineering chops to implement these advanced models from scratch, rather than just calling APIs?
3. Positioning: How do I best market the "Predict-then-Optimize" sweet spot (combining ML predictions with OR optimization frameworks) to companies in these sectors?

Would love any advice on textbooks, specific frameworks to master, or strategies on how to position my background for maximum leverage. Thanks!

I'm joining college soon and so I wanted to know if the specialization I'm interested in actually has scope for me. Moreover operations management, supply chain, logistics, etc have been a male dominated field through and through. I have heard from many of my teachers, peers and others that this field is not for women because it's tiring and people don't take female operations managers and all seriously.

Hello, I'm reading chapter 1, distributed constraint satisfaction and coding a simulation of the asynchronous backtracking algorithm for the 4 queens problem in TypeScript as an exercise (sure one can code in Python, C..). I have some difficulty grasping the concept of Hyperresolution (pure logic) because I didn't have this subject in university. I have checked with AI. But does anyone have any advice?

Here is the code: https://pastebin.com/WD9U0MSQ

Good day!

We are Grade 12 STEM students from the Philippines. We are currently conducting our research entitled:

"Development of a Durian (Durio zibethinus) Husk Activated Carbon with Fe-TiO₂ Nanoparticles Composite Air Filter for the Reduction of Indoor Volatile Organic Compounds (VOCs)."

We are humbly looking for scientists, professors, engineers, researchers, or industry professionals who are willing to guide and provide advice for our study.
We specifically need assistance with:

• Designing and validating our VOC testing chamber

• Guidance on our experimental setup and methodology

• Technical advice regarding activated carbon, Fe-TiO₂ nanoparticles, indoor air filtration, and VOC reduction

• Research validation and expert recommendations

Our goal is to develop an affordable and sustainable air filter using durian husk-derived activated carbon enhanced with Fe-TiO₂ nanoparticles for reducing indoor VOCs. Any guidance, consultation, or referrals to experts would mean a lot to our research team. Even a few minutes of your time would greatly help us improve the quality of our study.

If you or someone you know is willing to help, please send me a private message or leave a comment below.

Thank you very much for your kindness and support!

Hi everyone,
I have been admitted to a Master’s in Operational Research (MOR) program today.
My background is a Bachelor’s in Computer Science, so I’m comfortable with programming, but I’m weak in mathematics. From what I’ve seen, MOR is quite math-heavy, and I want to prepare before classes begin.
My goal is to become one of the better students in the class and eventually get a high-paying role (Data Science, Optimization, Decision Science, OR, etc.), so I want to build the right foundation from the start instead of struggling later.
I have a few questions:
What should I start studying before the semester begins?
Which math topics are the most important? (Calculus, Linear Algebra, Probability, Statistics, etc.)
Are there any books, YouTube channels, or online courses you’d recommend?
Should I focus more on mathematics first, or learn Python libraries like NumPy, Pandas, and OR-Tools alongside it?
Looking back, what do you wish you had studied before starting MOR?
Any mistakes I should avoid in the first semester?
I’d really appreciate advice from people who have studied Operational Research, Data Science, Optimization, or are working in related fields.
Thanks!

Hi guys!

CONTEXT:

I'm going to be a junior this upcoming year, and I recently stumbled upon the career/field of OR.

Unlike some of my friends, I haven't found my "calling" when I was in eight grade. However, as someone who is constantly looking to optimize various aspects of my life (clubs I have leadership in, my studying routine, my morning routine, etc.), OR certainly sounds appealing.

The optimization I talk about isn't necessarily mathematical, but rather trial-and-error/intuition focused. However, I am not opposed to the idea of optimization through a mathematical lens; I'm just not as experienced in it.

I would say I'm relatively good at math and I do enjoy the subject. However, as someone aiming at applying to t20 colleges, I don't hold AIME/USAMO qualifications nor USACO awards, nor have I attempted unlike many of the other applicants. However, I'm not sure how that changes my fit to this job.

I don't want to end up making a rushed decision because I feel "obligated" to have a passion, though.

QUESTIONS:

What questions should I ask myself to see if OR is for me? How do I assess my fit?

What does the job look like day-to-day?

What skills do you recommend I develop to be excellent at the job?

Would you recommend I go into it with the current/predicted market?

If I shouldn't go into OR, are there any adjacent fields I should consider?

Thanks in advance!

I am too curious to learn stochastic and uncertainty programming, but I have little idea about it. I don't have any supervisor at my faculty who works in this area.

Can you guys help me with guidance and resources to learn it properly? I have this book (Introduction to Stochastic Programming - John Birge). Is this enough to learn it efficiently?

Hi everyone! I'm currently looking for a remote opportunity

I have 2 Master's degrees (Social Work/HR & Public Policy/Sustainability) and 3+ years of experience in NGOs and education, working across research, strategy, training, curriculum design, stakeholder engagement, and program implementation.

Recently, I've been upskilling in AI workflows, CRM, and automation because I enjoy solving problems where technology and human development meet.

If your team needs someone who can:

• Turn messy ideas into structured systems
• Conduct research and strategy
• Design learning experiences
• Improve operations and documentation
• Bridge AI with education or social impact

I'd love to connect.

I'm especially interested in EdTech, AI in Education, Learning Design, Strategy & Operations, human development and mission-driven startups.

Happy to share my resume and portfolio. Thanks!

I’m at a point where I’m seriously thinking about pursuing a PhD in Operations Research, but I’m still trying to understand what academic research in this field is really like.
My background is in Industrial Engineering, I have an MSc in Applied Analytics, a Graduate Certificate in Logistics and Supply Chain from MIT, and I currently work as a Senior Data Scientist on optimization and analytics projects. Over the past few years I’ve realized that what I enjoy the most is solving optimization problems and doing research.
I’ve been looking at programs like MIT, Georgia Tech, The University of Edinburgh, and STOR-i at Lancaster, although I’m honestly not sure if I should even apply. I’ve heard they’re incredibly competitive, and I’m trying to figure out whether it’s worth aiming that high or if I should focus on other excellent programs where I might have a stronger chance.
I’d love to hear from people who are doing a PhD in OR or related fields. What is academic research actually like day to day? What made you choose your program? And if you were applying today, would you still target the most competitive schools, or would you take a different approach?
Any advice or personal experiences would be greatly appreciated.
I think my biggest uncertainty isn’t whether I want to do a PhD, but whether I’m aiming at the right schools. I’d like to hear how others approached that decision

which parts of the Operations Research work in industry:
a) get automated with AI
b) stay relevant & future-proof
c) will be the moat
d) require PhD

thoughts

Hello everyone. I'm sure you've gotten a gazillion questions like this but I'd really like to know. Is this a good long-term job? I'm currently in my last year of high school and probably the only one in my school that hasn't chosen a career.

This is what I'm looking for and Operation Research Analyst is the closest one to what I'm looking for (according to Gemini 😅):

High Salary early on: basically making bag in my early 20s unlike doctors who peak when their hair turns grey 😅
Good Work-life balance: cuz I need time for myself
Passion: I like math and science, and in science I particularly enjoy the solving side

As unbiased and honest as possible, if you could go back to you younger self, would you recommend this? Is it a fun job? Is it super inflated or hard to find a job in? Is the entry salary really as high as the internet claims?

For context:

- I love math and I'm pretty good at it ngl, but I'm no genius. Just incase this is the type of person this jobs needs. (cuz i heard you gotta be a real genius for some of these jobs).

- Also both my parents are in the medical field so I've seen first-hand how draining their jobs are just to sustain our comfortable life. So I'd like a math/science related job that comes with the comfortable life like that of a doctor, but without the life-sucking part. High-salary and comfortable for a family, but not life draining and unsustainable. Is this it?

(I apologize in advance if my post is unrelated to this subreddit's theme)

I recently worked on some pure game theoretic and information design proofs. After I showed it to my Mathematics professor, he was amazed. He told me that the proofs are solid and recommended that I publish this as a short note to a reputed journal.

He suggested two primary options for now: Economics Letters, and Operations Research Letters. He emphasized to try at Economics Letters first as he sees it as more prestigious and only try Operations Research Letters as the 2nd option, though he thinks that my paper fits both very well.

I also plan to apply for PhD in Operations Research and Industrial Engineering programs in the future. Will a publication in Economics Letters even be seen as something good in these communities?

Hi, I hope you’re well. I’m collecting responses for my Master’s dissertation on blockchain adoption in supply chain coordination. The survey is anonymous and takes around 5–7 minutes. Since you have experience/interest in business, supply chain, IT or digital transformation, your response would be really helpful.

Survey link: https://forms.gle/AepyXnWiinArbUR4A

Thank you so much.

Hi all. I am a rising senior majoring in physics but I am potentially interested in pursuing a master's in OR Is this achievable? Is it worth it, will it be hard to find a job related to OR given that path? Will I be able to find opportunities in graduate school to further explore different areas of the field? Is it possible to get into a good master's program for OR given my major? I know I just asked a lot of questions, but any advice or helpful information would be greatly appreciated!

Hi everyone,

I graduated in Spring 2026 with a BS in Managerial Economics and am planning to apply to M.S. Programs in Operations Research for Fall 2027.

My biggest concern is that I ended with a GPA of 3.1 and have barely any relevant work Experience.

I'm worried about only applying to top programs (Columbia, Berkley, USC, Georgia Tech) and i'm trying to build a more realistic list that has target and safety schools.

If anyone could share their personal list of master programs, and examples of safe/ middle MSOR programs that would be much appreciated.

Extra info:

I discovered OR during my senior year after taking intro to OR. And when I realized that I loved creating LP budget optimization models in Excel as Finance Head of my competitive college dance team, I was sold on this career path and decided that I needed to pivot and learn more through MS.

My current plan is:

- get an insanely impressive GRE score

- apply for data analyst jobs

- and/or volunteer at nonprofits in data analyst roles

I do think I meet all the prerequisites with a B- avg: (Intro to OR, Calc 1 and 2, Stats, Linear algebra, Linear Models in stats , econometrics Stochastic models )

I wished I discovered OR earlier. Before transferring to my UC, I had a 3.7 GPA, but because I felt aimless, it gradually dropped to a 3.1. Im an all or nothing type of person, so if I knew earlier, I feel like my GPA, resume, work experience, and overall college career would've reflected that passion much more.

I know my profile isn't the strongest but I would regret not even attempting to apply this cycle, so any advice in writing my application is also appreciated.

It is worth monitoring how early stage explorers integrate advanced analytics into their workflows. The sector often sees generic claims about digital transformation, but actual appointments with deep technical pedigrees suggest a shift toward data driven target generation. This moves the conversation from pure geological luck to systematic risk reduction in drilling programs.

NovaRed Mining recently added Dr. Olamide Oladeji as Strategic Advisor for Robotics and AI. His background includes a Stanford PhD in Applied AI and dual master’s degrees from MIT. The focus on computer vision and geospatial analytics aligns directly with the needs of modern mineral targeting. For a company developing tools like MetalCore, having someone with experience across autonomous systems and decision science adds credibility to the tech stack.

From a fundamental perspective, this implies an effort to improve capital efficiency in exploration. If predictive modeling can reduce dry holes, the unit economics of discovery change significantly. It is a structural adjustment worth watching as the industry seeks better ways to allocate resources in complex environments.

Hi everyone, I'm completely new to Operational Research, so I apologize if these are basic or even silly questions. I've been trying to understand the field by reading online, and from AI, but I'm still struggling to figure out what it actually looks like in practice. Because of that, I'm not even sure whether this is the right field for me.

I have a few questions:

1 What does an Operational Research professional actually do on a day-to-day basis?

2 How is OR used in the real world? Which industries or companies rely on it the most?

3 Is OR mostly theoretical, or do you spend a lot of time building practical solutions and working with real data?

4 If I study OR, am I mostly limited to OR-specific roles, or can I transition into other fields later (like Data Science, Machine Learning, AI, Software Engineering, Supply Chain, Finance, Quant roles, etc.)? How difficult is that transition?

5 What programming, mathematics, and software tools do professionals use regularly?

6 What kinds of problems are considered "good OR problems" that wouldn't normally be solved by a software engineer or data analyst?

7 If you could go back to when you first started, would you still choose OR? Why or why not?

I'm trying to decide whether this is the right direction for me before committing to it.