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Recommendation for a misplaced (hopefully) soon-to-be Ph.D.

Hello all!

I am at present near ending my Ph.D. within the intersection of Knowledge and Community Science (which mainly interprets to: we use information to resolve community science issues and check out our greatest to make sense of the outcomes). I am additionally a Telecom. Engineer by coaching, though my Ph.D. is technically in Laptop Science.

A lot of the information I gained throughout my Ph.D. has been on the Community Science (Graph Concept) aspect of issues, doing duties like hyperlink prediction, clustering, and so on. Now, I am near the tip and I am fairly set on shifting on from Academia to trade, however I really feel misplaced. I just like the technical a part of my job, but in addition speaking and making displays for different folks. Nevertheless, I am afraid that community science is just not very a lot utilized in many corporations.

I do know that in a Ph.D. you study greater than the sector itself you carry out your analysis on, however I want to ask you for recommendation. Have you ever seen Community Science utilized in the actual world (aside from recommender techniques)? In that case, through which sector? Additionally, how would you promote it to a possible employer?


Comments ( 5 )

  1. I’d focus more on leveraging the various technical skills you acquired in your PhD program rather than trying to leverage your domain expertise in a relatively small niche. I’m sure there are opportunities out there in network analysis, but you’re probably going to have better luck casting a wider net and going outside your subfield.

    Your goal should be to get a job that utilizes your skills, not find the perfect job for your niche. That’s not to say you don’t want to play up your expertise. But brand and market yourself beyond your immediate subfield.

    I have a friend who trained to be an economic historian. Unsurprisingly (to this labor economist), she had difficulty finding an academic job as economic history isn’t exactly a large subfield. It wasn’t until she rebranded herself as an applied econometrician rather than cliometrician that she found a good job outside academia working for a firm that mostly does contract work for the Bureau of Labor Statistics.

  2. Network optimization is a huge space within logistics for setting up cost-effective and resilient supply chains. Most DS I know in this space come from operations research or applied math backgrounds (and are the only data scientists I know who actually use Matlab as a tool in addition to Python), but your network science and telecom background might translate helpfully for some roles.

    Fraud detection is another possible application area to explore — think picking up fraudulent actors from payment data networks of transactions or connections on a social network.

  3. Yes, LinkedIn uses network science, for instance, and developed their own graph database.

    I think you need to network with other PhDs in your niche area.

    Also, there are research scientist positions; the thing is that many of the places traditionally doing the hiring are on freeze.

    It shouldn’t be hard to sell your skills, the issue is (a) do you know the tech stack used in industry? (b) finding the companies and positions that are the best fit between what you want & skills & who’s hiring; this is going to take research from your part.

  4. Cyber security could be an area to check out. Anomaly detection and network security. Also analysing logs.

    Plenty of work in your area but as a previous user has mentioned, you need to sell your diversity to your potential employer. Take a look at what kind of data they might have and give an example of how you can apply your skills.

    P.s. when writing ‘potential’ the word was autocorrected to ‘potentiometer’. I’ve learnt a new word today. Thanks Reddit user ☺️

  5. I think that simply having a technical PhD will open a lot of doors for you. It demonstrates that you went through academic rigor and most likely possess the ability to figure things out.

    Plus, as others have mentioned, graph theory may be very useful to logistics such as supply chain (lots of opportunities here), + hedgies, etc.

    Congrats on approaching the end of the journey!

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