Career Advice: Medical Devices or Data Science/Machine Learning

[outdoor_more]

Hello Bogleheads! I've come to respect this community and thought I might ask some career questions and see what I get

I'm currently about halfway through a PhD in engineering that prepares me quite well for two paths I have interest in: neurotechnology and data science/machine learning.

I'm very interested in using electrical stimulation to modulate the nervous system and treat pain, repair nerve damage, etc. One example is a spinal cord stimulator to reduce pain.

I'm also very interested in data science/machine learning. I enjoy using data to solve problems and answer complex questions. I enjoy the scientific process. I also feel like the field may be a bit of a bubble. I don't think it will ever die off, but I think the anticipation of what it will do might be slightly higher than it's actual uses.

My PhD provides good training in both of these areas. I'm trying to figure out the path I want to pursue when I finish up my PhD. I'm not saying I can't blend the two, but I haven't found many options that would do that. Turning to hear what the boglehead community has to say about that.
Long-term, I see myself more in leadership/managerial positions. I enjoy working with people. If I have a good team, I'm happy working on just about anything. I also enjoy the idea of having flexibility in my schedule. I'm a family-oriented person and want to be there for soccer games, music recitals, etc. I also really enjoy the outdoors and want to have close access to mountains. Not much of a big city person.

Below are pros/cons of each field (in my very subjective opinion which is always evolving). If you disagree with a statement, please share. Ones I'm not too sure of are marked with (?).

Med devices/neurotechnology:
- Pros:
- Benefits humanity through improvement of health (my altruistic side)
- Once you're in, employment is stable (?)
- Cons:
- Lack of flexibility (?)
- Medical technologies are slow moving (FDA)
Data science/machine learning:
- Pros:
- Involves a lot of new learning
- Get to solve complex problems with rigorous thought
- Flexibility with when/where you work
- Faster pace
- Cons:
- Lack of upward mobility (?)
- Less interaction with teams (?)

TL;DR
Engineering PhD looking for something in neurotechnology or data science/machine learning (or both). Long-term seeking leadership positions and flexible positions allowing time with family. Pros/cons of each above.

[leftcoaster]

Hello Bogleheads! I've come to respect this community and thought I might ask some career questions and see what I get

I'm currently about halfway through a PhD in engineering that prepares me quite well for two paths I have interest in: neurotechnology and data science/machine learning.

I'm very interested in using electrical stimulation to modulate the nervous system and treat pain, repair nerve damage, etc. One example is a spinal cord stimulator to reduce pain.

I'm also very interested in data science/machine learning. I enjoy using data to solve problems and answer complex questions. I enjoy the scientific process. I also feel like the field may be a bit of a bubble. I don't think it will ever die off, but I think the anticipation of what it will do might be slightly higher than it's actual uses.

My PhD provides good training in both of these areas. I'm trying to figure out the path I want to pursue when I finish up my PhD. I'm not saying I can't blend the two, but I haven't found many options that would do that. Turning to hear what the reddit community has to say about that.
Long-term, I see myself more in leadership/managerial positions. I enjoy working with people. If I have a good team, I'm happy working on just about anything. I also enjoy the idea of having flexibility in my schedule. I'm a family-oriented person and want to be there for soccer games, music recitals, etc.

Below are pros/cons of each field (in my very subjective opinion which is always evolving). If you disagree with a statement, please share. Ones I'm not too sure of are marked with (?).

Med devices/neurotechnology:
- Pros:
- Benefits humanity through improvement of health (my altruistic side)
- Once you're in, employment is stable (?)
- Cons:
- Lack of flexibility (?)
- Medical technologies are slow moving (FDA)
Data science/machine learning:
- Pros:
- Involves a lot of new learning
- Get to solve complex problems with rigorous thought
- Flexibility with when/where you work
- Faster pace
- Cons:
- Lack of upward mobility (?)
- Less interaction with teams (?)

TL;DR
Engineering PhD looking for something in neurotechnology or data science/machine learning (or both). Long-term seeking leadership positions and flexible positions allowing time with family. Pros/cons of each above.

Machine Learning is a branch of algorithms. You can develop expertise in that branch and even do novel research. But your professional experiences will be overwhelmingly dictated by the domain in which it’s applied. ML can be used to optimize life insurance profits or to create autonomous systems. Huge difference between those extremes.

Data science is indeed buzz wordy. Lots of companies have tons of data. Knowing how to analyze that data and build inferences is important. At the low end, this is just report writing. At the high end, it’s strategic - operational efficiency, market analysis, etc.

Much rests on the industry and role you take. Operations versus R&D can be quite different.

I’m puzzled that you think answering questions with data would have less team interaction. Your career advancement will be a function of innovation + domain mastery + relationships translated into business value.

As to the flexibility about when/where you work... you’re not going to advance without being part of a team and that requires face time and lots of it.

[quantAndHold]

Not sure where you get lack of interaction or upward mobility with data science. I also don’t think it’s in a bubble. The amount of data available is expanding exponentially, making it possible to solve new types of problems practically daily. Given the types of companies that hire data scientists vs medical device people, I would expect data science to pay better and have more room for refactoring your career later if you aren’t happy.

That said....you have some engineering skills that most pure data scientists don’t have. If it were me, I would try to find something that leveraged both areas of knowledge.

[outdoor_more]

Machine Learning is a branch of algorithms. You can develop expertise in that branch and even do novel research. But your professional experiences will be overwhelmingly dictated by the domain in which it’s applied. ML can be used to optimize life insurance profits or to create autonomous systems. Huge difference between those extremes.

Data science is indeed buzz wordy. Lots of companies have tons of data. Knowing how to analyze that data and build inferences is important. At the low end, this is just report writing. At the high end, it’s strategic - operational efficiency, market analysis, etc.

Much rests on the industry and role you take. Operations versus R&D can be quite different.

I’m puzzled that you think answering questions with data would have less team interaction. Your career advancement will be a function of innovation + domain mastery + relationships translated into business value.

As to the flexibility about when/where you work... you’re not going to advance without being part of a team and that requires face time and lots of it.

Thanks for the response. I'm not so much interested in the research with ML as I am with the applications. I understand that the domain entirely dictates the problems.

I guess when I said less team interaction, I meant that more along the lines of face-to-face, with remote work being more normal in the tech space than the device space (not that devices can't be largely engineered remotely).

Great insights! Thanks for sharing.

[ohai]

You know better, but it seems to me that every person at all level of education claims to be doing "data science" or is considering it (whatever that means). I can't help but get the impression that it is trendy and over saturated, and now I almost roll my eyes when some new person claims "serious interest" in the field - granted, most don't have your education level.

In the mean time, doctors keep charging me for every new thing without even asking, and it's hard to see the limit of the market for medical technology, provided that people can still invent new devices.

My uneducated impression anyway.

[outdoor_more]

Not sure where you get lack of interaction or upward mobility with data science. I also don’t think it’s in a bubble. The amount of data available is expanding exponentially, making it possible to solve new types of problems practically daily. Given the types of companies that hire data scientists vs medical device people, I would expect data science to pay better and have more room for refactoring your career later if you aren’t happy.

That said....you have some engineering skills that most pure data scientists don’t have. If it were me, I would try to find something that leveraged both areas of knowledge.

Maybe bubble isn't quite the right word, I'm just thinking of the gartner hype cycle and that we haven't completely passed over inflated expectations...

The networking I've done seems to indicate better pay in data science, and I agree it will probably be easier to reshape a career with the data science experience.

I totally am aiming to find something that leverages both areas - something healthcare-AI related potentially. I haven't quite found what I'm looking for yet, though.

[outdoor_more]

You know better, but it seems to me that every person at all level of education claims to be doing "data science" or is considering it (whatever that means). I can't help but get the impression that it is trendy and over saturated, and now I almost roll my eyes when some new person claims "serious interest" in the field - granted, most don't have your education level.

In the mean time, doctors keep charging me for every new thing without even asking, and it's hard to see the limit of the market for medical technology, provided that people can still invent new devices.

My uneducated impression anyway.

That's partly what the whole over-hyped thing is for me, as well. There are a lot of people doing it because there's virtually limitless data now. I do think the field is trendy, but I haven't gotten the impression of saturation. I do think that will come eventually, but I could be pretty well established by that point.

Thanks!

[KlangFool]

OP,

1) If you are doing a PhD in engineering, you are doing research in some specific area. So, why would you get a general job instead of the job in the specific area of your research?

2) If you have a Ph.D. in engineering, why would you try to compete with folks in the Data Science/Machine Learning area? Why are you abandoning your key advantage (Engineering) and fight in someone's else area?

<< Long-term, I see myself more in leadership/managerial positions. I enjoy working with people. If I have a good team, I'm happy working on just about anything. >>

3) Why are you doing your Ph.D. in Engineering? In general, people doing a Ph.D. because they want to work in a specific area. And, they know what they are good at technically.

4) If you are not doing your Ph.D. in the USA, please let us know. So far whatever you posted does not make any sense for a Ph.D. Engineering candidate in the USA.

KlangFool

[campy2010]

I see this as a one way street decision. If you want to use your degree to make med devices then you should do it now because returning to it later will probably be very hard or impossible. Using data to answer questions and solve problems is a more general skill that can be developed on top of domain knowledge. Med device companies are very data driven so even if you're not the one doing the data science/machine learning, it is possible to shift into a role where you're driving the questions even if someone else is doing the coding.

If I were you, I would take a break from your PhD program for a summer and do an internship in the area where you're leaning. You'll build connections and experience first hand. PM me if you want some suggestions on the health AI side of things.

[quantAndHold]

Not sure where you get lack of interaction or upward mobility with data science. I also don’t think it’s in a bubble. The amount of data available is expanding exponentially, making it possible to solve new types of problems practically daily. Given the types of companies that hire data scientists vs medical device people, I would expect data science to pay better and have more room for refactoring your career later if you aren’t happy.

That said....you have some engineering skills that most pure data scientists don’t have. If it were me, I would try to find something that leveraged both areas of knowledge.

Maybe bubble isn't quite the right word, I'm just thinking of the gartner hype cycle and that haven't completely passed over inflated expectations...

The networking I've done seems to indicate better pay in data science, and I agree it will probably be easier to reshape a career with the data science experience.

I totally am aiming to find something that leverages both areas - something healthcare-AI related potentially. I haven't quite found what I'm looking for yet, though.

The very, very large tech companies hire all kinds of people with interesting backgrounds, to do all kinds of things that you wouldn't think of them spending money on. I don't work for them anymore, but reading your original post, that's what I thought of.

[potatopancake]

You are considering some good options. Congrats on the PhD! Can you clarify the medical device route? I am familiar with medical device sales and this can be a flexible career that will allow you family time and the ability to live somewhere close to the outdoors. You mention a bubble for data science / machine learning. I agree the term 'machine learning' is en vouge and a bubble phrase at this point. Data science is a solid career and will allow you to pursue leadership positions in the future. Most industry positions will be in larger cities.

One piece of consideration: look past the intellectual draw. These careers are different in a few ways: who you work with on a daily basis, where you will be able to work, opportunity for career development, flexibility, requirements to schmooze with other professionals.

Best of luck with your decision. I think either option is excellent. You will always be able to switch if you find yourself professionally unhappy after a couple of years.

[outdoor_more]

OP,

1) If you are doing a PhD in engineering, you are doing research in some specific area. So, why would you get a general job instead of the job in the specific area of your research?

2) If you have a Ph.D. in engineering, why would you try to compete with folks in the Data Science/Machine Learning area? Why are you abandoning your key advantage (Engineering) and fight in someone's else area?

<< Long-term, I see myself more in leadership/managerial positions. I enjoy working with people. If I have a good team, I'm happy working on just about anything. >>

3) Why are you doing your Ph.D. in Engineering? In general, people doing a Ph.D. because they want to work in a specific area. And, they know what they are good at technically.

4) If you are not doing your Ph.D. in the USA, please let us know. So far whatever you posted does not make any sense for a Ph.D. Engineering candidate in the USA.

KlangFool

1) The specific area of my research doesn't have many industry options. I'm working on neural prostheses for upper limb amputees, which is a super fascinating project, but there aren't really enough amputees in the world to create a big industry out of it (thankfully). That said, techniques we use (neural stimulation/recording and machine learning) provide more broad options.

2) My PhD has involved a lot of what the DS/ML people are using. I think a large portion of DS is domain knowledge, which I could bring in the healthcare AI space.

3) I'm doing my PhD in Engineering because of the interest I have in the project I'm on. It's been a good experience, but there aren't many options outside of academia for continuing. I'm not very interested in academia.

4) My PhD is in the USA. Sometimes it doesn't quite make sense to me either...

[outdoor_more]

I see this as a one way street decision. If you want to use your degree to make med devices then you should do it now because returning to it later will probably be very hard or impossible. Using data to answer questions and solve problems is a more general skill that can be developed on top of domain knowledge. Med device companies are very data driven so even if you're not the one doing the data science/machine learning, it is possible to shift into a role where you're driving the questions even if someone else is doing the coding.

If I were you, I would take a break from your PhD program for a summer and do an internship in the area where you're leaning. You'll build connections and experience first hand. PM me if you want some suggestions on the health AI side of things.

Thanks for sharing. The ideal would be to blend the two. Unfortunately, taking a break would be pretty tough right now.

[outdoor_more]

Not sure where you get lack of interaction or upward mobility with data science. I also don’t think it’s in a bubble. The amount of data available is expanding exponentially, making it possible to solve new types of problems practically daily. Given the types of companies that hire data scientists vs medical device people, I would expect data science to pay better and have more room for refactoring your career later if you aren’t happy.

That said....you have some engineering skills that most pure data scientists don’t have. If it were me, I would try to find something that leveraged both areas of knowledge.

Maybe bubble isn't quite the right word, I'm just thinking of the gartner hype cycle and that haven't completely passed over inflated expectations...

The networking I've done seems to indicate better pay in data science, and I agree it will probably be easier to reshape a career with the data science experience.

I totally am aiming to find something that leverages both areas - something healthcare-AI related potentially. I haven't quite found what I'm looking for yet, though.

The very, very large tech companies hire all kinds of people with interesting backgrounds, to do all kinds of things that you wouldn't think of them spending money on. I don't work for them anymore, but reading your original post, that's what I thought of.

I've gotten an inkling of that (Microsoft Health comes to mind).

[outdoor_more]

You are considering some good options. Congrats on the PhD! Can you clarify the medical device route? I am familiar with medical device sales and this can be a flexible career that will allow you family time and the ability to live somewhere close to the outdoors. You mention a bubble for data science / machine learning. I agree the term 'machine learning' is en vouge and a bubble phrase at this point. Data science is a solid career and will allow you to pursue leadership positions in the future. Most industry positions will be in larger cities.

One piece of consideration: look past the intellectual draw. These careers are different in a few ways: who you work with on a daily basis, where you will be able to work, opportunity for career development, flexibility, requirements to schmooze with other professionals.

Best of luck with your decision. I think either option is excellent. You will always be able to switch if you find yourself professionally unhappy after a couple of years.

Thank you!

Medical device route: Greatest interests probably in product development. I've pretty strongly considered sales, and heard having a PhD can definitely benefit sales (both for having the degree and for having additional technical background).

I'd love to hear your experience on some of those points you mentioned beyond intellectual draw. I've tried to take those into consideration as well. In the networking I've done, the data science community seems to be a very open, engaged, and proactive community. I haven't noticed that as much for the med device community around where I live, but that may be largely due to the fact that the tech scene is pretty big and the med device scene is pretty small.

[KlangFool]

OP,

1) If you are doing a PhD in engineering, you are doing research in some specific area. So, why would you get a general job instead of the job in the specific area of your research?

2) If you have a Ph.D. in engineering, why would you try to compete with folks in the Data Science/Machine Learning area? Why are you abandoning your key advantage (Engineering) and fight in someone's else area?

<< Long-term, I see myself more in leadership/managerial positions. I enjoy working with people. If I have a good team, I'm happy working on just about anything. >>

3) Why are you doing your Ph.D. in Engineering? In general, people doing a Ph.D. because they want to work in a specific area. And, they know what they are good at technically.

4) If you are not doing your Ph.D. in the USA, please let us know. So far whatever you posted does not make any sense for a Ph.D. Engineering candidate in the USA.

KlangFool

1) The specific area of my research doesn't have many industry options. I'm working on neural prostheses for upper limb amputees, which is a super fascinating project, but there aren't really enough amputees in the world to create a big industry out of it (thankfully). That said, techniques we use (neural stimulation/recording and machine learning) provide more broad options.

2) My PhD has involved a lot of what the DS/ML people are using. I think a large portion of DS is domain knowledge, which I could bring in the healthcare AI space.

3) I'm doing my PhD in Engineering because of the interest I have in the project I'm on. It's been a good experience, but there aren't many options outside of academia for continuing. I'm not very interesting in academia.

4) My PhD is in the USA. Sometimes it doesn't quite make sense to me either...

outdoor_more,

1) Why whatever you are doing in your research does not apply to robotic?

2) If I am you, I would try to get an internship in Microsoft Research, Google, Amazon, and IBM Research. Aka, those companies that combined research and industry application.

KlangFool

[potatopancake]

Thank you!

Medical device route: Greatest interests probably in product development. I've pretty strongly considered sales, and heard having a PhD can definitely benefit sales (both for having the degree and for having additional technical background).

I'd love to hear your experience on some of those points you mentioned beyond intellectual draw. I've tried to take those into consideration as well. In the networking I've done, the data science community seems to be a very open, engaged, and proactive community. I haven't noticed that as much for the med device community around where I live, but that may be largely due to the fact that the tech scene is pretty big and the med device scene is pretty small.

Okay, that all makes sense. I read your other comment responses and it sounds like healthcare AI is a major interest. This is a cool, growing area of work. I will say that a chunk of this research is based within larger academic hospitals which is not the route you want. There is some industry development. There are some established players like Google (google health) and IBM (watson). You can certainly check their offerings for internships/employment. Start-ups are another option. Some start ups, prominently focused on imaging, have FDA approvals for implementation in healthcare settings (aidocs). Personally, clinical decision support within electronic health records seems like a sub-topic that may interest you. Check out Medvice. Otherwise, I know Siemens hires data scientists for healthcare algorithm projects.

Leadership is going to be more difficult coming into a start-up with a PhD. I would look more at big industry to find a position that you can grow into and find some success for promotion. I think there will be a number of positions you'll be able to find that fit your criteria. Good luck!

[outdoor_more]

outdoor_more,

1) Why whatever you are doing in your research does not apply to robotic?

2) If I am you, I would try to get an internship in Microsoft Research, Google, Amazon, and IBM Research. Aka, those companies that combined research and industry application.

KlangFool

1) Our work focuses on the interface between the human and the robot, for the most part. We use the robots, but not much more than high-level programming positions, reading from sensors, etc.

2) I would really enjoy that! My circumstances in the program will make that pretty tough to do, though. I'll definitely look more into making that work!

[outdoor_more]

Okay, that all makes sense. I read your other comment responses and it sounds like healthcare AI is a major interest. This is a cool, growing area of work. I will say that a chunk of this research is based within larger academic hospitals which is not the route you want. There is some industry development. There are some established players like Google (google health) and IBM (watson). You can certainly check their offerings for internships/employment. Start-ups are another option. Some start ups, prominently focused on imaging, have FDA approvals for implementation in healthcare settings (aidocs). Personally, clinical decision support within electronic health records seems like a sub-topic that may interest you. Check out Medvice. Otherwise, I know Siemens hires data scientists for healthcare algorithm projects.

Leadership is going to be more difficult coming into a start-up with a PhD. I would look more at big industry to find a position that you can grow into and find some success for promotion. I think there will be a number of positions you'll be able to find that fit your criteria. Good luck!

Yeah, healthcare AI is where I feel like I could blend two of my passions. I don't know that I would be against a position in an academic hospital, I'm just not wanting to be a professor anytime soon. I've heard some talk of google health and watson, so I'll have to see if I can make any connections there and get a feel for the teams and work going on.

I'll look into your other suggestions. Thanks again for the feedback!

[KlangFool]

outdoor_more,

1) Why whatever you are doing in your research does not apply to robotic?

2) If I am you, I would try to get an internship in Microsoft Research, Google, Amazon, and IBM Research. Aka, those companies that combined research and industry application.

KlangFool

1) Our work focuses on the interface between the human and the robot, for the most part. We use the robots, but not much more than high-level programming positions, reading from sensors, etc.

2) I would really enjoy that! My circumstances in the program will make that pretty tough to do, though. I'll definitely look more into making that work!

outdoor_more,

1) No idea. But, it is too early to write this off without further research.

2) If you do not try, it is definitely impossible. Does your University have a joint research program with one of those companies? Does your advisor know someone in those companies?

KlangFool

[il0kin]

Machine learning, a very key part of data science, is automating itself more and more every day.

I work in healthcare, in data, but I’m the guy who pulls data for data scientists to use. Statistics aren’t my thing; I like living in SQL.

Learn Python and R and build a ML model using an API from one of the many US or Intl government resources in both languages. If you can do that on your own and bring it to an employer, you will be employed in a 100k+ job as fast as you can sign offer letters.

We are only beginning to understand how to operationalize the incredible amount of data that is generated. If you position yourself to make profitable insights on that data, I think you will be gainfully employed for many years to come.

[JD2775]

I work in healthcare, in data, but I’m the guy who pulls data for data scientists to use. Statistics aren’t my thing; I like living in SQL

Me too! Data Warehouse engineer working with SQL and Python. The rest of that stuff is over my head

[dogperson]

Have you researched the companies that would be potential employers in each field, and imagined what your life would be like working there? With undergrads I find they tend to think about the work they want to do more than the kind of company and location where they want to work. You have to consider what aspects of work and life matter more to you. (I thought I wanted to work in a manufacturing environment till I realized how much I like working downtown.)

I know that the medical device companies tend to send pretty good-sized contingents to women’s engineering conferences. Supporting diversity & inclusion, and employee development, is usually a good sign for work-life balance, which seems important to you based on your original post.

Data science could take you in a lot more directions but it seems like Google employees work more hours than Medtronic employees. Of course, upper management works more hours at many companies.

I’d also consider where the companies tend to be (if there’s a pattern), since location near mountains is a priority for you.

The podcast How To with Charles Duhigg had an episode called “Uproot Your Life” where a professional poker player gave advice on how to make bets in life, such as trying to decide whether you’d be happier in one place or another. You might see if that helps you think through it.

[VictorStarr]

I would advice you to try find a company where you can directly use your experience in neurotechnology and machine learning. I am sure that there a few companies in Bay area working in this direction. It is better to start at project where you have a domain expertise. Later you can branch to other domains. There is a big difference between using ML in research projects vs applying ML in large scale products in industry.

[outdoor_more]

Machine learning, a very key part of data science, is automating itself more and more every day.

I work in healthcare, in data, but I’m the guy who pulls data for data scientists to use. Statistics aren’t my thing; I like living in SQL.

Learn Python and R and build a ML model using an API from one of the many US or Intl government resources in both languages. If you can do that on your own and bring it to an employer, you will be employed in a 100k+ job as fast as you can sign offer letters.

We are only beginning to understand how to operationalize the incredible amount of data that is generated. If you position yourself to make profitable insights on that data, I think you will be gainfully employed for many years to come.

Great insights, thanks. The project you describe sounds very doable! I'd have it done extra fast if I could do it in Matlab...

[outdoor_more]

Have you researched the companies that would be potential employers in each field, and imagined what your life would be like working there? With undergrads I find they tend to think about the work they want to do more than the kind of company and location where they want to work. You have to consider what aspects of work and life matter more to you. (I thought I wanted to work in a manufacturing environment till I realized how much I like working downtown.)

I know that the medical device companies tend to send pretty good-sized contingents to women’s engineering conferences. Supporting diversity & inclusion, and employee development, is usually a good sign for work-life balance, which seems important to you based on your original post.

Data science could take you in a lot more directions but it seems like Google employees work more hours than Medtronic employees. Of course, upper management works more hours at many companies.

I’d also consider where the companies tend to be (if there’s a pattern), since location near mountains is a priority for you.

The podcast How To with Charles Duhigg had an episode called “Uproot Your Life” where a professional poker player gave advice on how to make bets in life, such as trying to decide whether you’d be happier in one place or another. You might see if that helps you think through it.

I've been doing that sort of research/networking pretty regularly for a year or so, mostly connecting with alumni through LinkedIn and chatting with them about their career, company environments, work-life balance, etc. So much depends on the company, that the pros/cons I listed were major generalizations. Polling bogleheads has been a more recent idea I thought I might try.

I'll check out the podcast - thanks for the recommendation!

[outdoor_more]

I would advice you to try find a company where you can directly use your experience in neurotechnology and machine learning. I am sure that there a few companies in Bay area working in this direction. It is better to start at project where you have a domain expertise. Later you can branch to other domains. There is a big difference between using ML in research projects vs applying ML in large scale products in industry.

That really would be the ideal. I haven't counted the Bay area out, but let's just say it isn't at the top of the list of places I want to live. If it's the best place I find for the position I want, I'd go.

[nisiprius]

They are both credible. Most careers follow paths like a ball in a pinball machine, and you are asking whether it will be better to flip the left flipper or the right flipper. Since you can't really tell, you might as well focus on "following your bliss*", decide which really feels to you like your "calling."

Work always sucks at times and careers are always full of good and back luck and frustration, etc.

You are probably fortunate enough to be able to shoot for the "self-actualization" level of Maslow's hierarchy of needs. I think wealth and job title and such are only at the "esteem" layer.



I have known more than one person who is fairly dissatisfied because they took the path based on "best career" (by external criteria), more or less achieved it, and found that they hated going to work even though it paid well.

*(I've never actually read the Joseph Campbell book, probably should if I'm going to quote a phrase from it).

[unclescrooge]

OP,

1) If you are doing a PhD in engineering, you are doing research in some specific area. So, why would you get a general job instead of the job in the specific area of your research?

2) If you have a Ph.D. in engineering, why would you try to compete with folks in the Data Science/Machine Learning area? Why are you abandoning your key advantage (Engineering) and fight in someone's else area?

<< Long-term, I see myself more in leadership/managerial positions. I enjoy working with people. If I have a good team, I'm happy working on just about anything. >>

3) Why are you doing your Ph.D. in Engineering? In general, people doing a Ph.D. because they want to work in a specific area. And, they know what they are good at technically.

4) If you are not doing your Ph.D. in the USA, please let us know. So far whatever you posted does not make any sense for a Ph.D. Engineering candidate in the USA.

KlangFool

1) The specific area of my research doesn't have many industry options. I'm working on neural prostheses for upper limb amputees, which is a super fascinating project, but there aren't really enough amputees in the world to create a big industry out of it (thankfully). That said, techniques we use (neural stimulation/recording and machine learning) provide more broad options.

2) My PhD has involved a lot of what the DS/ML people are using. I think a large portion of DS is domain knowledge, which I could bring in the healthcare AI space.

3) I'm doing my PhD in Engineering because of the interest I have in the project I'm on. It's been a good experience, but there aren't many options outside of academia for continuing. I'm not very interested in academia.

4) My PhD is in the USA. Sometimes it doesn't quite make sense to me either...

Sounds like you need to start your own company. I think the name SkyNet is available.

I buddy of mine does biomedical research. He has multiple patents on identifying cancerous brain cells and making them glow in the dark for easy removal. He's worked on the biology portion as well as the engineering portion. He works long hours, often by himself or with a handful of his staff.

He's involved in 3 companies and one of them should be going public late this year. It looks like he'll make $20 million from his share. Great return for 15 years worth of work.

On the other hand, I work in data analytics and my company employs and slew of data science people. The work is fun, and fast paced. If you're leading a team, you're probably getting RSUs and eventually you'll probably be worth millions too. But we are not saving lives.

[outdoor_more]

Sounds like you need to start your own company. I think the name SkyNet is available.

I buddy of mine does biomedical research. He has multiple patents on identifying cancerous brain cells and making them glow in the dark for easy removal. He's worked on the biology portion as well as the engineering portion. He works long hours, often by himself or with a handful of his staff.

He's involved in 3 companies and one of them should be going public late this year. It looks like he'll make $20 million from his share. Great return for 15 years worth of work.

On the other hand, I work in data analytics and my company employs and slew of data science people. The work is fun, and fast paced. If you're leading a team, you're probably getting RSUs and eventually you'll probably be worth millions too. But we are not saving lives.

I'll see if I can find that domain

Your friend sounds like he's in a great position right now. Never know what the future holds!

The general impression I've gotten is that the data science crowd seems to enjoy their work quite a bit.

[outdoor_more]

They are both credible. Most careers follow paths like a ball in a pinball machine, and you are asking whether it will be better to flip the left flipper or the right flipper. Since you can't really tell, you might as well focus on "following your bliss*", decide which really feels to you like your "calling."

Work always sucks at times and careers are always full of good and back luck and frustration, etc.

You are probably fortunate enough to be able to shoot for the "self-actualization" level of Maslow's hierarchy of needs. I think wealth and job title and such are only at the "esteem" layer.

I have known more than one person who is fairly dissatisfied because they took the path based on "best career" (by external criteria), more or less achieved it, and found that they hated going to work even though it paid well.

*(I've never actually read the Joseph Campbell book, probably should if I'm going to quote a phrase from it).

Thanks for sharing. Part of what I'm trying to figure out is where my "bliss" is vs. where extrinsic factors are shaping that bliss. I don't know if we can ever fully know that, but that's why I'm trying to do my research

I do think both (or a combination) could be truly rewarding paths. Like I said in my initial post, I can be happy doing just about anything as long as I have a good team to work with. I was a roofer for several years during undergraduate years (generally miserable work), but loved it because I really enjoyed those I worked with.