apohn (u/apohn) - Readit News

apohn commented on Analysis of the data job market using HN job posts emiruz.com/post/2023-08-1... · Posted by u/usgroup

apohn · 3 years ago

While I generally agree, I think there's a point in these 2 statements that can easily misinterpreted.

>It is likely that the Data Scientist role is in a long term decline...

Also

> Data science is in decline and vaguely defined

Reading this, you can think that "Data Science" jobs are decreasing. But I don't think that's true.

Let's just say that it's 2017 and I hire a team of 3 people with the job title of Data Scientist. One ends up focusing on the data side, one on modeling+analysis, and one on building the infrastructure. In 2023, I decide to change the job titles so one of them is now a Data engineer, one is now a Data Scientist, and one is now a ML Engineer to match what is happening in the job market.

It's still 3 jobs with 3 people doing the same thing. So the number of jobs aren't decreasing, but their titles are more specific. Overall, the number of "Data Science" jobs are still doing up.

Somebody will say "But that's exactly what the author said." But I think people who are new(ish) to this field might read it as "Data Science Jobs are decreasing." So I'm making this comment.

> skills such as data mining and visualisation are also out of favour.

Honestly, I just don't believe this. It's possible that as job descriptions are filled with different buzzwords, people just leave these out. For visualization it's also possible that there is a bigger focus on keywords of an established BI tool (e.g. PowerBI) instead of ad-hoc charts in matplotlib or ggplot. But some degree of data mining and visualization is useful, even to Data Engineers.

apohn commented on Analysis of the data job market using HN job posts emiruz.com/post/2023-08-1... · Posted by u/usgroup

IKantRead · 3 years ago

With 10+ years in DS, I've always felt that best DS were always basically software engineers that knew math and were more interested in prototyping cool machine learning product than maintaining production infrastructure. Unfortunately this always accounted for a small fraction of DS I interacted with.

The largest group of DS was non-ML/CS/Math PhDs who started panicking once they realized their future job prospects in academia were very slim and so they signed up for bootcamps and got jobs at places hiring DS by the hundreds. Many of the people in this latter group had no idea how to write Python outside of a notebook, generally just structured problems to fit into XGBoost, and when not doing that tried to squeeze resume-boosting-complexity into any problem the could find. They also tended to have a hilariously poor understanding of creating business value.

Nearly everyone I know in the first group has switched back to just being an engineer of some sort, typically ML or AI engineer. I suspect the small set of talented people from the second group will end up in lesser paying product analytics type roles or closer to product management roles, while the majority that don't bring much to the table other than a PhD will be slowly attritioned out of the field as companies start looking for the value different skillsets bring to the table.

apohn · 3 years ago

>With 10+ years in DS, I've always felt that best DS were always basically software engineers that knew math and were more interested in prototyping cool machine learning product than maintaining production infrastructure. Unfortunately this always accounted for a small fraction of DS I interacted with.

I've been a DS for 10+ years, and I feel the exact opposite. The worst "Data Scientists" I've worked with are all ex Software Engineers who seem to assume that business problems are really computation problems. So they find convenient ways to ignore the human aspects (e.g. trying to figure out why the data is a mess) and gravitate to using more complex algorithms and breaking down the problem to an achievable programming pipeline that runs in production, but the results are of low value. But it looks awesome on a resume.

Are you right or am I right about SWEs turned DS? I have no idea. But one quality that IMHO is important is the interest in actually looking at data and asking questions, which is much rarer than most people realize.

apohn commented on Ask HN: Is it still possible in 2023 to build competitive AI startup? · Posted by u/MaxRush

apohn · 3 years ago

There might be a lesson from history on where an "AI" startup needs to focus if they want to succeed.

Starting around 2012, there was a huge hype around ML. Lots of startups on selling "ML." If you look today, the majority of the startups that just sold ML algorithms or ML in a box are pretty much gone, or were acquired for amounts where only the founders made enough money to pay off their car loan on a Toyota Camry. At least one of the most highly regarded Unicorns in 2014 now is in the "Wait, that company still exists?" category.

The companies that survived or got acquired for a decent amount were the companies that used ML to actually solve a tangible problem. They were not selling ML, but a solution to some problem that happened to use ML - even if their marketing focused on the ML part.

I wouldn't be surprised to see the same with AI companies.

A big company (e.g. Google) can quickly release a similar product for an LLM because that's an area where the big company was already doing work and a lot of the knowledge to build it is published or known. But if a startup is targeting a focused problem in a specific industry, a big company can't just wake up one day and get the information needed to solve that problem in a short amount of time.

apohn commented on Classical ML Still Relevant? · Posted by u/Sanej

apohn · 3 years ago

One of the ways I think about this type of problem is by asking "You want to use computation to extract a signal from this data. What's that signal worth to you in business ROI dollars?"

If Domain Expertise + Feature Engineering + ML model can get you 90% of the way there and it runs on a tiny cloud instance that takes 30 minutes to train, is a DL based approach that pushes you to 91% worth it from an ROI instance if takes a 4xGPU cluster 2 days to train it, not to mention inference costs? Especially if you need to explain what the model is doing?"

This above is exactly the situation I'm in now with my job. I'm on the "Get useful stuff to production so we can save money" side of things, and we have R&D teams who try to approach the same problems using DL and all the latest methods. At least for the use cases our team focuses on, they haven't been able to do more than set $$$ on fire via GPUs. For us, Domain Knowledge + Good Data Engineering is the secret.

I think ML is going to be around for a long time because it works, even though DL is dominating the news right now. Just because a neurologist can also diagnose and treat common medical conditions (e.g a pneumonia), that doesn't mean we need every doctor to be neurologist.

apohn commented on Classical ML Still Relevant? · Posted by u/Sanej

fdgsdfogijq · 3 years ago

"text classification it depends on the problem but often the old methods work very well and there is not a lot of room for neural methods to do better."

This couldnt be further from the truth. NLP/text algorithms have seen model improvements from NNs more than any other field.

apohn · 3 years ago

>This couldnt be further from the truth.

I think one thing to keep in mind is that there are specific use cases where the cost of using DL isn't worth the improvement in accuracy (if there is one) from a business ROI perspective.

I know somebody who works in the insurance industry on a text classification use case. The business impact of this use case is important as it's used as part of the claims process. The team he's on has tried a lot of different things, but feature engineering + domain expertise + a particular tree ML model has provided the best performance for the lowest overall cost. They are very open to trying new things, but a DL approach simply hasn't been worth it.

apohn commented on Ask HN: How do Sales Engineers evolve their careers? · Posted by u/kjellsbells

apohn · 3 years ago

>But (especially in Enterprise B2B) they're not coders, and they're not product managers, and they are not quite salesperson "enough" to qualify for evolving to any of those career positions.

I was in PreSales for a long time and IME, this isn't true (excluding SWEs). Different SEs have different personalities and different inclinations. Some of these personality traits means they have to stop being an SE and do something else to maximize their career satisfaction.

A lot of SEs stay as SEs and simply grow as technical experts or industry experts and then go into a "Principal" SE role. I think these tend to be the type of people who like go deep into something and their strengths are in going deep. The ones who get tired of the sales part can go into developer relations, solution development, etc where they are part of building solutions, but they are removed from the heavy sales parts of it. You can certainly get paid enough as a Principal level that money is removed as a reason for changing career paths.

There's then the type of SE who is strong technically and is good at understanding what a customer is really asking for and translating that into a solution to a specific use case. These are the ones who can be good product managers. I put myself in this category and the product SWEs I worked with usually liked talking to me because I could frame a customer ask in a concrete technical way, which was something a lot of Product Managers struggled with. I had more than one instance where a Product Manager said if I wanted to switch to Technical Product Management they could find a path to make that happen.

There's then the type of SE who is okay technically, but who really cares about customers succeeding in the long term. They can do well in customer success roles, or even technical support and management roles in either of those areas.

There's then type of SE who is really enjoys the "educating" part of sales. They can go into the Training/Education side of things.

Finally, there's the type of SE who has a great personality, but they are poor technically and not very detail oriented when it comes to anything technical. I've seen these become sales people or move into account management.

I've worked with SEs who have moved into Sales roles, Product Management, Training, Customer Success, and PreSales Management, Developer Relations, etc.

Regarding SWE, I do agree with you that transitioning from SE to SWE is harder. I've worked with SWEs who became SEs but couldn't transition back. Once you've climbed out of the trenches and seen the big picture it's hard to say I'm going to go back in the trenches, especially at a large B2B company where there are 1000s of SWEs. It's easier if you were a specialist (e.g. in Data Science) and the SWE job is a specialist role.

>How do you evolve SEs in your org?

One thing I've seen is that many SE managers are sales people at heart, which makes them terrible at evolving SEs. They only reward and recognize the SEs who have the traits that overlap with sales people. So they can't even recognize how to evolve their SEs. Even if the SE manager gets it, the Directors/VPs, etc are all ex-sales people and they don't get the value of focusing on these other things.

IMO the way to evolve SEs is to connect them with somebody from a different org (that can use the SEs strengths) and have them work together. The SE will learn about how people do a different job, and that other person gets a direct line into what SES are seeing in the field. It's generally a net positive for the company.

apohn commented on Ask HN: Math books that made you significantly better at math? · Posted by u/optbuild

scruple · 3 years ago

Calculus Made Easy and Probability Through Problems. I'm not sure that I'd have gotten through either my university Calculus courses or Probability and Statistics without these two books. I used them as supplementary material to the course textbooks and homework. They both have a style that is approachable and helped me build an intuition for the material unlike anything else I found.

apohn · 3 years ago

>Probability Through Problems

First time I'm hearing about this one, thanks for the recommendation. Unlike Calculus or even a typical one semester Statistics course, probability is one of those topics where you need to see a lot of problems to really grok anything. The only way is to see a lot of solved problems and think about why that's the right answer.

Even highly recommended books (e.g. by Blitzstein) don't have enough solved problems, so it's nice to there's a problem focused book out there.

apohn commented on Ask HN: Have you made a career move “down” on purpose, and how has it been? · Posted by u/throw_booored

apohn · 3 years ago

In the last 5 years I've moved from Data Science Manager to Principal (IC role, but basically the external facing technical lead of the team) and now Senior. When I add up all the positives and negatives at work and at home, I think I'm the most content I've been in a long time.

One piece of advice I can give is to make a list of concrete things that are making you think about becoming an IC. And by concrete I don't mean just a word like "Stress." List out the things that are causing that (e.g. Unpredictable rushed deadlines). If it's useful, create a mental Venn diagram of what overlaps and pull out some larger themes. From the list, pick the top 2 - 3 and ask yourself if you are really going to get that as an IC.

Example: For me, one thing that was causing me an immense amount of stress was a highly unpredictable schedule - last minute meetings, fires, people with no ability to prioritize, etc. At the top of my list was being able control my schedule to a reasonable degree. IME that's really hard to do as a Manager or even a Lead/Principal, so it made sense to me to move down to Senior and find a job that allowed for that.

That being said, there are plenty of IC jobs where you sit in pointless meetings all day and get called into meetings at the last minute. It's not like moving down is a guarantee of anything, so it's important to really identify if moving down is going to get you what want. HN has plenty of anecdotes of crappy IC jobs.

As far as pay, I took a small haircut from Manager -> Principal and then a slightly bigger haircut from Principal -> Senior. My spouse works so we still have more than enough to pay bills, meet retirement goals, and have something left over for other stuff. I'm not paid even close to what FAANG people are making, but I suspect lots of people I work with on the business side of things would love to be in my salary band.

apohn commented on The last three years of my work will be permanently abandoned ericlippert.com/2022/11/3... · Posted by u/chubot

ericlippert · 3 years ago

I'm 100% sincere in that praise of my colleagues.

Many people, myself included, had a lot of concerns about the products the company was building and their effects on the world. When you work on a team whose mission is to help other teams make better decisions at lower cost, the aim is to look at the whole system and improve the whole thing.

Let me give you an example. Most "this content doesn't belong on FB" decisions are made by ML, but a great many go to human review. Imagine what that job is like. It's emotionally exhausting, it's poorly compensated, burnout is high.

My team had a model in production where we would use Bayesian reasoning to automatically detect when a particular human was likely to have made the correct decision about content classification, and therefore, if two humans disagreed, how to resolve that impasse without getting a third involved. (And in addition we get a lot more information out of the model including bounds on true prevalence of bad content, and so on.)

Does that save the company money? Sure. Millions of dollars a month. (And for the amateur bean counters elsewhere on this page: the data scientist who developed this model is NOT PAID MILLIONS OF DOLLARS A MONTH.) But it also (1) helps keep bad content off of the platform, so users aren't exposed to it, (2) lowers the number of human reviewers who come into contact with it, which is improves their jobs, and (3) frees up budget for whatever improvements need to be made to this whole workflow.

That's just one example; everything that we did was with an eye towards not merely saving the company money, but improving the ability to make good decisions about the products.

apohn · 3 years ago

>But it also (1) helps keep bad content off of the platform, so users aren't exposed to it, (2) lowers the number of human reviewers who come into contact with it, which is improves their jobs, and (3) frees up budget for whatever improvements need to be made to this whole workflow.

I think reading that this type of solution was created, and person who worked on it was laid off, makes me very sad as a Data Scientist.

I enjoy working as a Data Scientist, but I struggle a lot with the field. Lots of jobs are mostly about grabbing eyeballs or selling something. Some jobs are just total bullshit. Even the ones where you're doing something concrete (e.g. keeping a machine running), some days you still wonder if it really matters in the long run.

But with some of these social media safety topics, it can feel like a job has some meaning beyond just shuffling numbers around on an spreadsheet.

So it's disappointing to hear that people with the skills to create something like that are fired.

apohn commented on The last three years of my work will be permanently abandoned ericlippert.com/2022/11/3... · Posted by u/chubot

nightski · 3 years ago

I think this is an insightful assessment. Not everyone in a company can be top line. But I also think there's a lot more opportunity in using statistics/ml/data science in the top line than most companies practice.

apohn · 3 years ago

>But I also think there's a lot more opportunity in using statistics/ml/data science in the top line than most companies practice.

I consider myself a fairly honest Data Scientist, in the sense that I like it when I can map what I'm doing to the value it delivers. I know some other great people I've worked with who are like this as well.

This is anecdotal, but all of us have hated working with many top line people because there's some really fuzzy mapping from goal to value (since value is realized in the long term), and some of the people are champion bullshitters. I don't need to explain sales people. But marketing, corporate strategy, and even upper product management - they drove us crazy because their standard of being data driven was absolutely not consistent with how we thought about things at all. All of it was because the mapping from project to revenue was over years, not quarters. And it was all projections.

Compare this to bottom line people, where the mapping from project to cost savings is on a shorter time frame. The types of personalities this attracts is different.

Maybe the growth hacking stuff at software companies is different and you can focus on revenue growth and still connect what you are doing to that. I've never worked in that role so I don't know.