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Having to deal with data scientists, I absolutely agree. The thing that I've seen that lands in the "lab" vs production distinction is that these people expect

by ziml77 6y ago

Having to deal with data scientists, I absolutely agree. The thing that I've seen that lands in the "lab" vs production distinction is that these people expect their data to be pristine. They flip out when the world isn't as perfect as their models want. Leads to me as just a normal software developer having to do the data analysis and figure out how to clean it up. I also end up having to be the one to talk to data vendors to understand their data feeds and essentially translate that for the data scientists. Having to sit in the middle is annoying for me and suboptimal for the business.

superbcarrot 6y ago
I think that this is more of a problem with the specific people that you have worked with and it isn't inherent to the role of a data scientist.
- ct0 6y ago
  Doesn't sound like a modern Data Scientist, sounds more like a statistician with 30+ years of experience.
- sidlls 6y ago
  It’s becoming more inherent, especially as the field is populated with people who have no experience with the “science” part. That is, with the very real and ubiquitous problem of collecting and cleaning data to make it fit for scientific study. Even theoretical physicists, for example, participate in and rely on empirical data collection, and understand deeply how messy and fraught with error it is. I don’t see the same appreciation or consideration in general in the field of data “science.”
  - superbcarrot 6y ago
    > with people who have no experience with the “science” part It's interesting that you put it that way because a lot of the other complaints in this thread are that the people who expect their data to be ready for use are exactly the people with science experience but without the relevant technical background.
  - Mauricebranagh 6y ago
    I remember working with some one who has PhD in Physics and who worked at CERN - and one comment I loved "a key skill is knowing how to place the legend, so it obscures that annoying outlier data point"
nerdponx 6y ago
Instead of sneering at "having to deal with" data scientists, consider that the data scientists themselves would often much rather have data engineers and dev ops people involved in the process. Data scientists like to quip that 80% of the job is data cleaning, with the remaining 20% divided up arbitrarily among other tasks as suited the joke. In some shops nowadays, it's more like 45% data cleaning, 45% data engineering/ops/programming just trying to make your results available to the rest of your organization, and 10% research. If I can spend less time learning/doing software engineering and devops and more time doing actual data science, that's great. At a previous job, my team was clamoring for more data engineer hiring, and part of the reason our projects were slipping and starting to fail was lack of data engineering support. Our tooling was shit, our processes were shit, our code was shit, and access to (and trust of) our data sources was especially wet and stinky shit. It made the daily work of doing data science a miserable slog of ad-hoc duct-tape solutions, and it contributed to us being generally ineffective as a team. All of this would have been fixed if we had one competent data engineer with some actual real-world data/ML engineering experience and good communication/advocacy skills. Let alone two or three!
- ramraj07 6y ago
  If the DE tooling was shit and you couldn't hire more fast enough, why didn't your team members start addressing these problems? Surely spending half the time cleaning up the pipes would increase the value of what you do with the other half?
glitchc 6y ago
This implies a lack of rigorous training. In the physical sciences, one wouldn't become an applied scientist without conducting an experiment to test a phenomenon, and the teeth gnashing that goes with making that experiment work. Those who have been fed pristine data without having to undergo the trials and tribulations of actually having to collect the data have missed a crucial part of scientific training. Like you, I find this lack of rigour is rather common among data scientists. Not all, but quite a few.
- dgb23 6y ago
  That's what I was wondering reading this thread. Much of science is dirty work in other fields and I think that is a good thing. How ridiculous to assume that a scientist doesn't clean their tools and set up their experiments. (Surely as one gets more experienced and older, the job likely becomes less manual, more about teaching and coordinating.)
astrophysician 6y ago
Just want to say that while the data science profession definitely includes a wide range of people and skillsets, a good data scientist should be practical and able to work with the available data in whatever state it's in. No good data scientist should ever expect data to be pristine. And a good data scientist, even if they don't have quite the engineering chops necessary to build a production-quality ETL, should know enough about the process to help guide it. If they aren't a part of that process, they're not being a good DS. They can't expect someone not involved with their problem to know what tradeoffs to make, and if they don't know exactly how their data went from raw form to the ETL-ed form, they're probably going to make bad assumptions, and those assumptions may very well make their architected solution a complete pile of garbage. Not to mention, how can a DS offer suggestions for solutions if they aren't deeply familiar with the raw data that's available? To me, a good data scientist should, at bare minimum, have several skills. * They should first and foremost (but not solely) be an in house expert in statistics and machine learning to know what can be done with data, and what can't be done with data. They should arrive with that knowledge. Engineers I think have a tendency to trivialize this, but true expertise in this domain comes only with years of experience. * They should strive to find modeling solutions that are right for a particular business problem. If they seem to be only applying the hottest research regardless of the tradeoffs for the particular business problem, that's a red flag. * Their focus should be on integrating themselves with the product/business as much as possible, and with the engineering team as much as possible. If they're expecting to be handed directives, that's a recipe for a ton of wasted time. DS should never, ever be siloed into their own little DS world. They will be useless without a deeply intimate knowledge of the business goals, the needs of product, and the capabilities of the engineering team. As they progress, they should become more and more "full-stack", otherwise they are stagnating.
- tchalla 6y ago
  A good data scientist should also be good at science. Otherwise, you can simply hire people with engineering skills - you don't need scientists. If you hire scientists and then are surprised they aren't good at engineering, the hiring process needs a reality check.
  - 6y ago
baron_harkonnen 6y ago
The data science field has been flooded with PhDs with nowhere else to go that have no background in engineering, and sadly often have a very poor understanding of both machine learning and statistics. Companies were in a rush hire "data scientists" and boot camps like Insight were more than happy to pump out very impressive PhDs with just enough understanding to build a Keras model. I've worked in industry awhile doing DS work and have been astounded at the number of PhDs that both don't know how to write Python that doesn't live in a notebook and throw away years of disciplined experimentation experiences to just throw keras models at data until the needle moves. There do exist excellent data scientists out there, who are both very solid software engineers and really know their stuff mathematically, but I've found most of these people can't reliably find jobs because the people interviewing them know so little that good data scientists will be penalized for answer a stock question correctly. The field has been so flooded with amateurs that have no idea what they're doing, that potential mentors have been driven out, and now it's just a mess. To get a job doing DS if you do know what you are doing you have to play a weird game where you guess the incorrect answer the interviewer has in mind.
- fock 6y ago
  I do an introductory Python lab course at my university. It's targeted at engineers who still create graphs from Excel and then normally level up to MATLAB, if things get complicated (think insets, ...). I guess about 30% of the people previously did at least some of the YT/Udemy "courses" on datascience. It's really horrifying for me (not being an engineer myself, but imo having a relatively engineering-like mindset) to see these people horrified at simple tasks like writing a variadic function. "What do I need this for?". Well, it's using the programming environment. And then let them code up a simple version of Levenberg-Marquardt. The level of "why do I need to do this" is astonishing again...
  - nitrogen 6y ago
    why do I need to do this IMO this is the number one problem of our modern culture around education. Popular culture makes it popular to treat education as pointless, and this even affects students who are pursuing difficult degrees. "Why do I need to study humanities? Why should I learn to code if I think I am born to be someone else's boss?" On the other hand, many teachers in K12 and early university have no ability to connect the "what" with the "why." "The curriculum is the curriculum. The test is the test." If we can solve these problems, our societies will be much better off.

by ziml77 6y ago

superbcarrot 6y ago

I think that this is more of a problem with the specific people that you have worked with and it isn't inherent to the role of a data scientist.

ct0 6y ago
Doesn't sound like a modern Data Scientist, sounds more like a statistician with 30+ years of experience.
sidlls 6y ago
It’s becoming more inherent, especially as the field is populated with people who have no experience with the “science” part. That is, with the very real and ubiquitous problem of collecting and cleaning data to make it fit for scientific study. Even theoretical physicists, for example, participate in and rely on empirical data collection, and understand deeply how messy and fraught with error it is. I don’t see the same appreciation or consideration in general in the field of data “science.”
- superbcarrot 6y ago
  > with people who have no experience with the “science” part It's interesting that you put it that way because a lot of the other complaints in this thread are that the people who expect their data to be ready for use are exactly the people with science experience but without the relevant technical background.
- Mauricebranagh 6y ago
  I remember working with some one who has PhD in Physics and who worked at CERN - and one comment I loved "a key skill is knowing how to place the legend, so it obscures that annoying outlier data point"

nerdponx 6y ago

Instead of sneering at "having to deal with" data scientists, consider that the data scientists themselves would often much rather have data engineers and dev ops people involved in the process. Data scientists like to quip that 80% of the job is data cleaning, with the remaining 20% divided up arbitrarily among other tasks as suited the joke. In some shops nowadays, it's more like 45% data cleaning, 45% data engineering/ops/programming just trying to make your results available to the rest of your organization, and 10% research. If I can spend less time learning/doing software engineering and devops and more time doing actual data science, that's great. At a previous job, my team was clamoring for more data engineer hiring, and part of the reason our projects were slipping and starting to fail was lack of data engineering support. Our tooling was shit, our processes were shit, our code was shit, and access to (and trust of) our data sources was especially wet and stinky shit. It made the daily work of doing data science a miserable slog of ad-hoc duct-tape solutions, and it contributed to us being generally ineffective as a team. All of this would have been fixed if we had one competent data engineer with some actual real-world data/ML engineering experience and good communication/advocacy skills. Let alone two or three!

ramraj07 6y ago
If the DE tooling was shit and you couldn't hire more fast enough, why didn't your team members start addressing these problems? Surely spending half the time cleaning up the pipes would increase the value of what you do with the other half?

glitchc 6y ago

This implies a lack of rigorous training. In the physical sciences, one wouldn't become an applied scientist without conducting an experiment to test a phenomenon, and the teeth gnashing that goes with making that experiment work. Those who have been fed pristine data without having to undergo the trials and tribulations of actually having to collect the data have missed a crucial part of scientific training. Like you, I find this lack of rigour is rather common among data scientists. Not all, but quite a few.

dgb23 6y ago
That's what I was wondering reading this thread. Much of science is dirty work in other fields and I think that is a good thing. How ridiculous to assume that a scientist doesn't clean their tools and set up their experiments. (Surely as one gets more experienced and older, the job likely becomes less manual, more about teaching and coordinating.)

astrophysician 6y ago

Just want to say that while the data science profession definitely includes a wide range of people and skillsets, a good data scientist should be practical and able to work with the available data in whatever state it's in. No good data scientist should ever expect data to be pristine. And a good data scientist, even if they don't have quite the engineering chops necessary to build a production-quality ETL, should know enough about the process to help guide it. If they aren't a part of that process, they're not being a good DS. They can't expect someone not involved with their problem to know what tradeoffs to make, and if they don't know exactly how their data went from raw form to the ETL-ed form, they're probably going to make bad assumptions, and those assumptions may very well make their architected solution a complete pile of garbage. Not to mention, how can a DS offer suggestions for solutions if they aren't deeply familiar with the raw data that's available? To me, a good data scientist should, at bare minimum, have several skills. * They should first and foremost (but not solely) be an in house expert in statistics and machine learning to know what can be done with data, and what can't be done with data. They should arrive with that knowledge. Engineers I think have a tendency to trivialize this, but true expertise in this domain comes only with years of experience. * They should strive to find modeling solutions that are right for a particular business problem. If they seem to be only applying the hottest research regardless of the tradeoffs for the particular business problem, that's a red flag. * Their focus should be on integrating themselves with the product/business as much as possible, and with the engineering team as much as possible. If they're expecting to be handed directives, that's a recipe for a ton of wasted time. DS should never, ever be siloed into their own little DS world. They will be useless without a deeply intimate knowledge of the business goals, the needs of product, and the capabilities of the engineering team. As they progress, they should become more and more "full-stack", otherwise they are stagnating.

tchalla 6y ago
A good data scientist should also be good at science. Otherwise, you can simply hire people with engineering skills - you don't need scientists. If you hire scientists and then are surprised they aren't good at engineering, the hiring process needs a reality check.
- 6y ago

baron_harkonnen 6y ago

The data science field has been flooded with PhDs with nowhere else to go that have no background in engineering, and sadly often have a very poor understanding of both machine learning and statistics. Companies were in a rush hire "data scientists" and boot camps like Insight were more than happy to pump out very impressive PhDs with just enough understanding to build a Keras model. I've worked in industry awhile doing DS work and have been astounded at the number of PhDs that both don't know how to write Python that doesn't live in a notebook and throw away years of disciplined experimentation experiences to just throw keras models at data until the needle moves. There do exist excellent data scientists out there, who are both very solid software engineers and really know their stuff mathematically, but I've found most of these people can't reliably find jobs because the people interviewing them know so little that good data scientists will be penalized for answer a stock question correctly. The field has been so flooded with amateurs that have no idea what they're doing, that potential mentors have been driven out, and now it's just a mess. To get a job doing DS if you do know what you are doing you have to play a weird game where you guess the incorrect answer the interviewer has in mind.

fock 6y ago
I do an introductory Python lab course at my university. It's targeted at engineers who still create graphs from Excel and then normally level up to MATLAB, if things get complicated (think insets, ...). I guess about 30% of the people previously did at least some of the YT/Udemy "courses" on datascience. It's really horrifying for me (not being an engineer myself, but imo having a relatively engineering-like mindset) to see these people horrified at simple tasks like writing a variadic function. "What do I need this for?". Well, it's using the programming environment. And then let them code up a simple version of Levenberg-Marquardt. The level of "why do I need to do this" is astonishing again...
- nitrogen 6y ago
  why do I need to do this IMO this is the number one problem of our modern culture around education. Popular culture makes it popular to treat education as pointless, and this even affects students who are pursuing difficult degrees. "Why do I need to study humanities? Why should I learn to code if I think I am born to be someone else's boss?" On the other hand, many teachers in K12 and early university have no ability to connect the "what" with the "why." "The curriculum is the curriculum. The test is the test." If we can solve these problems, our societies will be much better off.