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Working in the ML/data space and have been fortunate to have largely steered clear of this problem so far. My heuristic when evaluating potential jobs is only t

by steppi 4y ago

Working in the ML/data space and have been fortunate to have largely steered clear of this problem so far. My heuristic when evaluating potential jobs is only to consider positions where the output of statistical analysis and machine learning models has a clear and immediate impact within the organization, while avoiding adtech for ethical reasons and because of concerns toxic work could enable a toxic environment. Jobs like this exist, though you may have to take a pay cut compared to the bullshit. My first job after finishing my PhD was as a staff scientist in an academic lab using ML engineering + data science to support scientific research. There seems to be a fair number of grant supported jobs like this and pay isn’t terrible. Just under or just at 6 figures. You can make more in industry, but scientific work feels very meaningful. Now I’m working in credit risk modeling, something I never expected to be doing, but so far it’s been a good fit. The models are applied directly in decision making for the business, and there’s a real incentive to get everything right because mistakes could harm real people’s lives. The team I’m on is strong and ethically sound and I feel good about what I’m doing. For anyone in the data space who’s despairing about the state of the industry, non-bullshit jobs do exist, you just have to look for them, and use your judgment when scoping out new roles.

pram 4y ago
I’ve been doing “big data” in the retail space for almost a decade now and it’s pretty clear to me how it affects the bottom line. It really can be as simple as “we need more of this milk” and “the spicy queso is very popular”
- sebastiansm 4y ago
  I'll really would like that the reatailer prepared for me my montlhy basket based on my previous and recurrent consumption.
  - ryanianian 4y ago
    And if stores had meals pre-shopped in bags you could just remove unwanted items from. Grocery stores seem ripe for simple optimizations even without big data
    - agentultra 4y ago
      Grocery stores would be better served by not being giant chains with optimization problems, at least in North America. Better zoning laws that allow mixed-use zoning would enable more, smaller grocers embedded in neighbourhoods. Personalized service would be much easier on a smaller scale. And you wouldn't have to travel far to pick it up.
      - the_lonely_road 4y ago
        This is a recipe for significantly higher grocery prices which is the exact opposite of what you want for one of the staples of stability: food.
        comte7092 4y ago
        I’d beg to differ. The current system optimizes for large grocery trips for bulk items that require long shelf stability. Most people would agree that produce should make up the bulk of ones diet, yet it’s pretty plain to see that the footprint of the produce section doesn’t scale with the size of the store. Produce is something best shopped for frequently, and is something I’d personally be willing to pay a higher upfront cost for the convenience of closer smaller stores where I can get in and out quickly. Because while we may pay a lower sticker price, the elephant in the room with the massive hidden cost that we pay but rarely account for: food waste.
        pixl97 4y ago
        You're also likely someone with a higher than average income, and general closeness to potential store locations. If you were poorer, or had to drive farther it's less likely you'd use those as optimization points. Also fresh food is great for the diet and health, but when some even occurs that interrupts things like distribution of fuel, food, or power just having it around may lead you to getting hungry really quickly. This is something you have to look at on both a personal and national level. Food security can quickly lead to destabilization.
  - schnable 4y ago
    Most online grocers do this.
  - nsxwolf 4y ago
    It's a great way to make sure people never try new things.
    - hgsgm 4y ago
      How so? It could include new recommendws things.
  - toss1 4y ago
    My household would really like it if Amazon foods merely provide the same list we bought form last time to adjust for this delivery — we absolutely hate having to start from scratch every time (and that's not even mentioning their issues with substitution). Heck, the local wine & beer outlet lets me just open up the last purchase list and buy that again (and adjust the quantities if desired). WTF is wrong when a local store can get it so right, yet Amazon, with it's emphasis on "always be hungry like a startup", so totally forks it up?
    - ghaff 4y ago
      Substitution was the real issue I had with online grocery delivery when I last used it 15 years ago. Things would get substituted that I didn't really think were equivalent and essential ingredients for some meal would be left off the order. It was "OK" at the time; I was on crutches and could go to the store but not easily do a full grocery shopping. But I haven't done online grocery since.
      - sizzle 4y ago
        15 years is a long time in tech
        ghaff 4y ago
        From what I hear from people, "tech" doesn't really solve the substitution or out of stock problem. I also mostly would just as soon go to the store.
- ghaff 4y ago
  I'm at least somewhat skeptical. The face of the last data warehousing fad in the 90s was around things like optimizing retail through things like putting diapers and beer [1] close together. But pretty much, even when there was some correlation, stores never did much about it. [1] https://canworksmart.com/diapers-beer-retail-predictive-analytics/ https://canworksmart.com/diapers-beer-retail-predictive-anal...
  - prewett 4y ago
    I can imagine that one reason is that you also need customers to be able to find things, which is difficult when not sorted into some meaningful categories. That said, I think at the Target near me the beer and the diapers are relatively close.
    - saulpw 4y ago
      Actually you want customers to not be able to find things, so they have to wander around, and 'find' other things (in large displays for example) that you want them to buy. If the beer and diapers are always together then they won't discover the new high-margin sausage rolls. So what's better for the customer from a correlated data perspective may not be better for the business from a maximizing profit perspective.
      - ghaff 4y ago
        Taken to the extreme though, I'm going to be taking up employee time asking where something is and eventually stop shopping at a store if it's too frustrating.
        saulpw 4y ago
        a) most people don't take things to extremes; b) employee time is not a problem, as they're there anyway; c) if all grocery stores act like this (and they do), you have to keep shopping there anyway (and they know this). So what will actually happen, with you and me and everyone else, is that we'll grumble about how they moved the cheese again, we'll wander around the store until we find it, and we'll be exposed to some more products and implicit advertising in our search, and we'll forget about it as soon as we exit the store. Over time the store will increase its profits 1.3%, and the competing store "where everything is always in the same place as last time" struggles because their prices aren't maximally optimized with the same grocery store software everyone else uses.
- civilized 4y ago
  I believe you, because the store is always out of the most popular products, and nobody ever seems to figure out that they could simply shift production towards the more popular products and make more money?
  - pixl97 4y ago
    You sure about that? On a local basis I can go to two of the same store a few miles apart and one will out of products X, Y, and Z, but the other store may be out of X, T, and R. Localized buying trends can have significant differences. Also, there may be many other effects here. For example, if a popular product is actually popular and the pipeline to make the product is months long, well there's going to be outages. Shifting production generally isn't easy and trends pass quickly. In addition, perceived popularity can be used to manipulate consumer pricing. "X is always out. Oh look X is in stock now, I should buy it for 50% more"
    - civilized 4y ago
      Yes, I am sure. I go to grocery stores in multiple locations and they are all much more likely to be out of a particular version of a product than other versions. It has been this way for years. They do not price this version differently than the other versions. And this is true for multiple products even in different kinds of stores. I think some suppliers just aren't that good at adapting supply to demand.
      - jstrong 4y ago
        > for years prior to covid? last few years have not been representative about how well the supply chain works over the past 50 years.
        civilized 4y ago
        Yep, it's been a thing in grocery stores for as long as I can remember. Maybe a little worse now.
- jhallenworld 4y ago
  This is funny to me. In the old days, this was called: cost accounting to determine the profit of any particular item you sell and market analysis to see how many of which things your customers are likely to buy. Join the two results to optimize your profits. It's not rocket (/data) science. Actually neither of these is particularly easy to do for unorganized companies.
thegginthesky 4y ago
I really like your heuristic, and I'll add to your list of examples where one could work - Any tech company where the Stats/ML model is one of THE products and differentiators. You'll need to cut through a lot of buzz word and sales-speak to find the good ones. - Banks and other financial institutions where making uneducated guess is a big no-no when it comes risk, pricing and anything related to financial products. Your example of Credit Risk Modeling is a classic example and very interesting problem. - As much as consultancy gets a bad rep due to some shady practice from big players, there exists a solid demand for professionals that know Statistics in the Large Construction Projects space. Let's say modeling demand and return financial for a project, proving environmental impact, preparing/implementing/analyzing unbiased surveys in the area, and so on. - Government agencies where data is one of the Key Outputs. Such as the Census, Bureau of Labor Statistics, CDC, and so many others. As you said, sometimes it will mean a pay cut, especially if you want to remain in the technical work and not deal with the business and managerial side of things. But there's solid demand.
- cirgue 4y ago
  +1 for government. I work with state and local governments a fair bit, and while there’s a lot of red tape and weird politics, the overwhelming majority of clients I work with are smart, capable, highly mission driven people doing their best in a system designed to move slowly. Being able to look back on a project and see a positive impact in a community is way more rewarding than trying to move the needle on click through. And the pay cut for public sector consulting isn’t as much as one would think. Especially for federal.
nuclearnice1 4y ago
Can you tell us a little about the models and software packages of credit risk? Is it trying to take a huge dataset of consumer features and join it to a dataset of loan outcomes and then predict loan outcomes?
- steppi 4y ago
  Yes, that's a common approach. You can take a dataset of consumer features at the point in time when loans were opened along with information about the loan outcomes and try to predict the loan outcomes. You can't just take a kitchen sink approach with the features though because there are regulations that demand a level of explainability. To get a sense of the basics, I think the book Intelligent Credit Scoring by Naeem Siddiqi [0] is very good. [0] https://towardsdatascience.com/book-review-intelligent-credit-scoring-by-naeem-saddiqi-334dacb001a9 https://towardsdatascience.com/book-review-intelligent-credi...
  - throwaway201606 4y ago
    This is a really good answer i would also like to add that modelling in credit risk is not just about yes / no answers around loan outcomes. There are lots of other goals that are regularly modelled such as default rates, profit optimization, loss minimization, delinquency and payoff rates at specific parameters ... endless options There are also lot of different ways in which these models are implemented ( decision trees, statistical analysis, ML... ) Some examples of (real life) projects include: - if our institution offers this card to clients with 750 credit scores vs 790 credit scores, how does my profit move vs my losses and what the factors to limit losses while maximizing profits - how do I minimize my costs for servicing this card while keeping profits at the max ? - what rewards options lead to the highest number of preselected / qualifying clients taking up a product at the lowest cost - what contact strategies are best for specific types of clients if they are late on payments - call or email or text or legal letter? which strategies are the cheapest? which strategies give what this institution considers to be the best response ? which lead to fastest full payment? fastest partial payment? which lead to getting back to a regular payment plan? - how can we identify clients who have a lending product with us who might be on the market for another lending product in the next 12 months? in the 6 months?, those who might need a limit increase pro-actively? those who whose might need a limit decrease pro-actively? And, one of the largest area pf credit risk evaluation is real time decisioning on transactions: 'is throwaway201606 really buying $6000 of apple products, in person, at this mall in Toronto, Canada right now when I (the system) know I he bought a daily Wendy's Spicy chicken sandwich 10 minutes ago in Dallas" and should we allow this payment Some example of how models are used here include ( note that modelling helps establish which transactions to look at more carefully and which to ban outright among other things ) - predict what type of terminals are being targeted: scammers -> we have left bank ATM machines alone and started looked at gas pumps: - predict where transactions of interest might come from: scammers -> we do scams on site A at Christmas, scams on site B in the summer or we do site A scams with brand Y card and do site B scams with brand Z card - predict behaviour patterns of transactions of interest: we always test the cards we will use by purchasing a $5 'brand x' gift card online 10 minutes before
fnands 4y ago
Yeah, I was basically filtering out jobs applications by whether or not I could see what effect ML was supposed to have on the functioning of the business, and importantly, whether or not they actually had access to the data to make it work. Ended up at a place where ML doesn't just make things better, but is necessary to make things work at scale. There were a lot of job ads I looked at that just seemed dreadful to me though. So many recruiting companies wanted ML Engineers/Data scientists. I could kinda see how it would work, but didn't think I would enjoy it.
ensemblehq 4y ago
Focusing on jobs where your output makes an immediate impact is an incredibly smart move to make sure you matter. Of course, most roles matter but there are too few managers who know how to vouch and articulate a team’s business value properly. I actually found myself working on a credit risk modelling project on the capital markets side and it’s been great as well.
- migf 4y ago
  This is actually the most important career advice for tech jobs. First junior dev job it's not as important because you just need to find something. After that you will want to be very careful to make sure the job has actual business impact, otherwise you are likely to end up in some kind of bullshit vanity project that only has to appear to work.
- pjdesno 4y ago
  If you're a baseball player, you don't want to work for a football team. You'll be undervalued and hate your job. Less metaphorically, I've always looked for jobs where my skills are aligned with the "company mission" - first at a bunch of startups, and now as an academic researcher where I get to define that mission.
arbuge 4y ago
I find it strange that AI/ML people would avoid "adtech for ethical reasons". I really wish the websites I visited made better use of my actual history on those sites to tailor relevant ads to my interests. It seems like an ideal application of AI to me. They could do a far better job than serving me the lowest common denominator stuff they keep throwing at me. My Twitter news feed these days: * 10% - posts of interest from people I follow, i.e. the stuff I actually go to Twitter for * 90% - Ads and recommendations of topics to follow that I have zero interest in If it's going to insist on showing ads and recommending topics of interest, you'd think those could be better personalized, given that Twitter has years of my tweet, reply, and like history to train its AI on. But no... what I get is crypto ads, Hollywood events, celebrity news, sports news, etc.
- jewayne 4y ago
  You understand that you're not the one paying for the service, so you don't get a say, right? Even once The Machine has perfect knowledge of you, the ads will not be tailored to your preferences. It will be what an advertiser has paid The Machine to show you.
  - arbuge 4y ago
    Actually I do get a say... take the Twitter example. In that case it's resulted in me visiting Twitter far less than I used to. I may not be voting with my dollars as a user here, but I am voting with my attention span. Dollars are not the only asset of value in play.
  - cratermoon 4y ago
    > what an advertiser has paid The Machine to show you. As Cory Doctorow has point out so eloquently in his "enshittification" series, the end point isn't even to the benefit of the advertiser. At the end state, The Machine also have perfect knowledge of the advertiser, and the adtech company can turn the full power of the The Machine to the benefit of itself, extracting value from both the audience and the advertisers.
- deleted 4y ago
  [deleted]
santoshalper 4y ago
When I was starting out my career, my father gave me a piece of advice that has turned out to be incredibly applicable and valuable - "Stay close to the money". What he meant by that is work in environments where the work you perform is very closely tied to the revenue of the company - or another way of saying it - "Do work that is directly part of the organization's mission". It sounds like you've applied this to your career with good results.
- mtnGoat 4y ago
  Yup, always try to work in the profit centers, not the cost centers. Was reminded of that a job or two ago in a big way.
- ludicity 4y ago
  Every time I don't listen to Patrick McKenzie, I am filled with regret.
im_down_w_otp 4y ago
As a non-ad tech example, a significant component to what we're trying to do at https://www.auxon.io https://www.auxon.io is provide tech to companies they can use for testing & analysis of robots and cyber-physical systems. From our perspective internally what we're getting is the ability to perform something akin to "materials science" for the increasingly critical software parts of these systems and construct models of their behavior to use for predictive & comparative analysis purposes. We have a research partnership with the University of Ottawa & University of Luxembourg specifically on the statistical analysis end of things to go deep in some areas to later incorporate the findings into our products. In fact the first go around of that research cycle has already happened (https://arxiv.org/abs/2301.13807v1 https://arxiv.org/abs/2301.13807v1) and the insights are being integrated into our Deviant product (https://auxon.io/products/deviant https://auxon.io/products/deviant). It's definitely not ad-tech. It definitely has a specific applied use case. Most of our marketplace traction is in aerospace, energy, automotive, and defense. We're not immediately hiring for roles on the data analysis end of things (we're in much more immediate need of visualization & frontend help), but we will be this year.
magicalhippo 4y ago
> a clear and immediate impact within the organization This reminded me of those Microsoft Viva emails I keep getting. Anyone finding those useful for anything at all? To me it seems like a solution in search of a problem.
- paavope 4y ago
  That definitely goes to the category of products that exist to keep a highly paid data/development team busy
sizzle 4y ago
Please come to the field of computational biology, we need your help finding molecules that cure cancer and other diseases
plaidfuji 4y ago
> My heuristic when evaluating potential jobs is only to consider positions where the output of statistical analysis and machine learning models has a clear and immediate impact within the organization, while avoiding adtech for ethical reasons and because of concerns toxic work could enable a toxic environment. Currently starting to look for a new role and couldn’t have articulated my goal more clearly. The problem is that this narrows the field considerably, to finance/insurance/fraud (“pure money work”), healthcare/EMR (which as far as I can tell is also actually just profit optimization work for hospitals), and then you have manufacturing (closer to where I currently am), but the actual applications of data science are more limited and data Eng / analytics are more valuable, bioinformatics (which is really interesting, but also a large learning curve), and then just traditional BI (kind of generic and boring). Also, if you’re thinking about long term career development, many of these functions fall under the IT/CIO umbrella as orgs grow, which means that career advancement would require learning cloud architecture, cybersecurity, IAM, networking, etc. which I’m not saying is a bad thing, just an observation. Just applying statistics and modeling can only rise so high in an org, unless like you said, their core business and value prop is being the best at modeling some phenomena.
- skrtskrt 4y ago
  anti-fraud is very unsexy work IMO, but as long as you believe that (insert some internet-enabled service) should exist, then it can't be safe and enjoyable to use without serious fraud mitigation and prevention work, you can be secure in knowing that you're making life a lot easier for people who otherwise would have suffered from that fraud
  - bglazer 4y ago
    Curious why you think antifraud is “unsexy”? I worked in the field for a few years. My experience was that the bulk of fraud is easily detected with bog standard models (most people doing fraud are lazy). But, there’s a long tail of super sophisticated work for detecting more subtle operators. It’s also pretty fun to learn about the convoluted schemes people come up with for doing fraud and to imagine how someone would attack a new service. Also theres quite a bit of money sloshing around and it’s relatively easy to quantify financial benefit from fraud reduction, so the departments tend to be well funded
ludicity 4y ago
I agree they do exist, and this heuristic sounds sensible to me. It's the good old patio11 "go work in a profit center" situation, and I wish I had done so. I'd probably be grappling with a new existential concern, but that's life (I wish I had a job that didn't matter so I wouldn't have to worry about performance all day!).
pfalke 4y ago
Well said! To add a few options - energy management (shifting loads to times when energy is cheap) for consumer/commercial/industrial use cases - energy markets, especially power trading: often highly algorithmic, and driven by models that turn fundamentals data (weather, calendar, …) into supply/demand predictions, and from there into price predictions - retail pricing, both offline and e-commerce

by steppi 4y ago

pram 4y ago

I’ve been doing “big data” in the retail space for almost a decade now and it’s pretty clear to me how it affects the bottom line. It really can be as simple as “we need more of this milk” and “the spicy queso is very popular”

sebastiansm 4y ago
I'll really would like that the reatailer prepared for me my montlhy basket based on my previous and recurrent consumption.
- ryanianian 4y ago
  And if stores had meals pre-shopped in bags you could just remove unwanted items from. Grocery stores seem ripe for simple optimizations even without big data
  - agentultra 4y ago
    Grocery stores would be better served by not being giant chains with optimization problems, at least in North America. Better zoning laws that allow mixed-use zoning would enable more, smaller grocers embedded in neighbourhoods. Personalized service would be much easier on a smaller scale. And you wouldn't have to travel far to pick it up.
    - the_lonely_road 4y ago
      This is a recipe for significantly higher grocery prices which is the exact opposite of what you want for one of the staples of stability: food.
      - comte7092 4y ago
        I’d beg to differ. The current system optimizes for large grocery trips for bulk items that require long shelf stability. Most people would agree that produce should make up the bulk of ones diet, yet it’s pretty plain to see that the footprint of the produce section doesn’t scale with the size of the store. Produce is something best shopped for frequently, and is something I’d personally be willing to pay a higher upfront cost for the convenience of closer smaller stores where I can get in and out quickly. Because while we may pay a lower sticker price, the elephant in the room with the massive hidden cost that we pay but rarely account for: food waste.
        pixl97 4y ago
        You're also likely someone with a higher than average income, and general closeness to potential store locations. If you were poorer, or had to drive farther it's less likely you'd use those as optimization points. Also fresh food is great for the diet and health, but when some even occurs that interrupts things like distribution of fuel, food, or power just having it around may lead you to getting hungry really quickly. This is something you have to look at on both a personal and national level. Food security can quickly lead to destabilization.
- schnable 4y ago
  Most online grocers do this.
- nsxwolf 4y ago
  It's a great way to make sure people never try new things.
  - hgsgm 4y ago
    How so? It could include new recommendws things.
- toss1 4y ago
  My household would really like it if Amazon foods merely provide the same list we bought form last time to adjust for this delivery — we absolutely hate having to start from scratch every time (and that's not even mentioning their issues with substitution). Heck, the local wine & beer outlet lets me just open up the last purchase list and buy that again (and adjust the quantities if desired). WTF is wrong when a local store can get it so right, yet Amazon, with it's emphasis on "always be hungry like a startup", so totally forks it up?
  - ghaff 4y ago
    Substitution was the real issue I had with online grocery delivery when I last used it 15 years ago. Things would get substituted that I didn't really think were equivalent and essential ingredients for some meal would be left off the order. It was "OK" at the time; I was on crutches and could go to the store but not easily do a full grocery shopping. But I haven't done online grocery since.
    - sizzle 4y ago
      15 years is a long time in tech
      - ghaff 4y ago
        From what I hear from people, "tech" doesn't really solve the substitution or out of stock problem. I also mostly would just as soon go to the store.
ghaff 4y ago
I'm at least somewhat skeptical. The face of the last data warehousing fad in the 90s was around things like optimizing retail through things like putting diapers and beer [1] close together. But pretty much, even when there was some correlation, stores never did much about it. [1] https://canworksmart.com/diapers-beer-retail-predictive-analytics/ https://canworksmart.com/diapers-beer-retail-predictive-anal...
- prewett 4y ago
  I can imagine that one reason is that you also need customers to be able to find things, which is difficult when not sorted into some meaningful categories. That said, I think at the Target near me the beer and the diapers are relatively close.
  - saulpw 4y ago
    Actually you want customers to not be able to find things, so they have to wander around, and 'find' other things (in large displays for example) that you want them to buy. If the beer and diapers are always together then they won't discover the new high-margin sausage rolls. So what's better for the customer from a correlated data perspective may not be better for the business from a maximizing profit perspective.
    - ghaff 4y ago
      Taken to the extreme though, I'm going to be taking up employee time asking where something is and eventually stop shopping at a store if it's too frustrating.
      - saulpw 4y ago
        a) most people don't take things to extremes; b) employee time is not a problem, as they're there anyway; c) if all grocery stores act like this (and they do), you have to keep shopping there anyway (and they know this). So what will actually happen, with you and me and everyone else, is that we'll grumble about how they moved the cheese again, we'll wander around the store until we find it, and we'll be exposed to some more products and implicit advertising in our search, and we'll forget about it as soon as we exit the store. Over time the store will increase its profits 1.3%, and the competing store "where everything is always in the same place as last time" struggles because their prices aren't maximally optimized with the same grocery store software everyone else uses.
civilized 4y ago
I believe you, because the store is always out of the most popular products, and nobody ever seems to figure out that they could simply shift production towards the more popular products and make more money?
- pixl97 4y ago
  You sure about that? On a local basis I can go to two of the same store a few miles apart and one will out of products X, Y, and Z, but the other store may be out of X, T, and R. Localized buying trends can have significant differences. Also, there may be many other effects here. For example, if a popular product is actually popular and the pipeline to make the product is months long, well there's going to be outages. Shifting production generally isn't easy and trends pass quickly. In addition, perceived popularity can be used to manipulate consumer pricing. "X is always out. Oh look X is in stock now, I should buy it for 50% more"
  - civilized 4y ago
    Yes, I am sure. I go to grocery stores in multiple locations and they are all much more likely to be out of a particular version of a product than other versions. It has been this way for years. They do not price this version differently than the other versions. And this is true for multiple products even in different kinds of stores. I think some suppliers just aren't that good at adapting supply to demand.
    - jstrong 4y ago
      > for years prior to covid? last few years have not been representative about how well the supply chain works over the past 50 years.
      - civilized 4y ago
        Yep, it's been a thing in grocery stores for as long as I can remember. Maybe a little worse now.
jhallenworld 4y ago
This is funny to me. In the old days, this was called: cost accounting to determine the profit of any particular item you sell and market analysis to see how many of which things your customers are likely to buy. Join the two results to optimize your profits. It's not rocket (/data) science. Actually neither of these is particularly easy to do for unorganized companies.

thegginthesky 4y ago

I really like your heuristic, and I'll add to your list of examples where one could work - Any tech company where the Stats/ML model is one of THE products and differentiators. You'll need to cut through a lot of buzz word and sales-speak to find the good ones. - Banks and other financial institutions where making uneducated guess is a big no-no when it comes risk, pricing and anything related to financial products. Your example of Credit Risk Modeling is a classic example and very interesting problem. - As much as consultancy gets a bad rep due to some shady practice from big players, there exists a solid demand for professionals that know Statistics in the Large Construction Projects space. Let's say modeling demand and return financial for a project, proving environmental impact, preparing/implementing/analyzing unbiased surveys in the area, and so on. - Government agencies where data is one of the Key Outputs. Such as the Census, Bureau of Labor Statistics, CDC, and so many others. As you said, sometimes it will mean a pay cut, especially if you want to remain in the technical work and not deal with the business and managerial side of things. But there's solid demand.

cirgue 4y ago
+1 for government. I work with state and local governments a fair bit, and while there’s a lot of red tape and weird politics, the overwhelming majority of clients I work with are smart, capable, highly mission driven people doing their best in a system designed to move slowly. Being able to look back on a project and see a positive impact in a community is way more rewarding than trying to move the needle on click through. And the pay cut for public sector consulting isn’t as much as one would think. Especially for federal.

nuclearnice1 4y ago

Can you tell us a little about the models and software packages of credit risk? Is it trying to take a huge dataset of consumer features and join it to a dataset of loan outcomes and then predict loan outcomes?

steppi 4y ago
Yes, that's a common approach. You can take a dataset of consumer features at the point in time when loans were opened along with information about the loan outcomes and try to predict the loan outcomes. You can't just take a kitchen sink approach with the features though because there are regulations that demand a level of explainability. To get a sense of the basics, I think the book Intelligent Credit Scoring by Naeem Siddiqi [0] is very good. [0] https://towardsdatascience.com/book-review-intelligent-credit-scoring-by-naeem-saddiqi-334dacb001a9 https://towardsdatascience.com/book-review-intelligent-credi...
- throwaway201606 4y ago
  This is a really good answer i would also like to add that modelling in credit risk is not just about yes / no answers around loan outcomes. There are lots of other goals that are regularly modelled such as default rates, profit optimization, loss minimization, delinquency and payoff rates at specific parameters ... endless options There are also lot of different ways in which these models are implemented ( decision trees, statistical analysis, ML... ) Some examples of (real life) projects include: - if our institution offers this card to clients with 750 credit scores vs 790 credit scores, how does my profit move vs my losses and what the factors to limit losses while maximizing profits - how do I minimize my costs for servicing this card while keeping profits at the max ? - what rewards options lead to the highest number of preselected / qualifying clients taking up a product at the lowest cost - what contact strategies are best for specific types of clients if they are late on payments - call or email or text or legal letter? which strategies are the cheapest? which strategies give what this institution considers to be the best response ? which lead to fastest full payment? fastest partial payment? which lead to getting back to a regular payment plan? - how can we identify clients who have a lending product with us who might be on the market for another lending product in the next 12 months? in the 6 months?, those who might need a limit increase pro-actively? those who whose might need a limit decrease pro-actively? And, one of the largest area pf credit risk evaluation is real time decisioning on transactions: 'is throwaway201606 really buying $6000 of apple products, in person, at this mall in Toronto, Canada right now when I (the system) know I he bought a daily Wendy's Spicy chicken sandwich 10 minutes ago in Dallas" and should we allow this payment Some example of how models are used here include ( note that modelling helps establish which transactions to look at more carefully and which to ban outright among other things ) - predict what type of terminals are being targeted: scammers -> we have left bank ATM machines alone and started looked at gas pumps: - predict where transactions of interest might come from: scammers -> we do scams on site A at Christmas, scams on site B in the summer or we do site A scams with brand Y card and do site B scams with brand Z card - predict behaviour patterns of transactions of interest: we always test the cards we will use by purchasing a $5 'brand x' gift card online 10 minutes before

fnands 4y ago

Yeah, I was basically filtering out jobs applications by whether or not I could see what effect ML was supposed to have on the functioning of the business, and importantly, whether or not they actually had access to the data to make it work. Ended up at a place where ML doesn't just make things better, but is necessary to make things work at scale. There were a lot of job ads I looked at that just seemed dreadful to me though. So many recruiting companies wanted ML Engineers/Data scientists. I could kinda see how it would work, but didn't think I would enjoy it.

ensemblehq 4y ago

Focusing on jobs where your output makes an immediate impact is an incredibly smart move to make sure you matter. Of course, most roles matter but there are too few managers who know how to vouch and articulate a team’s business value properly. I actually found myself working on a credit risk modelling project on the capital markets side and it’s been great as well.

migf 4y ago
This is actually the most important career advice for tech jobs. First junior dev job it's not as important because you just need to find something. After that you will want to be very careful to make sure the job has actual business impact, otherwise you are likely to end up in some kind of bullshit vanity project that only has to appear to work.
pjdesno 4y ago
If you're a baseball player, you don't want to work for a football team. You'll be undervalued and hate your job. Less metaphorically, I've always looked for jobs where my skills are aligned with the "company mission" - first at a bunch of startups, and now as an academic researcher where I get to define that mission.

arbuge 4y ago

I find it strange that AI/ML people would avoid "adtech for ethical reasons". I really wish the websites I visited made better use of my actual history on those sites to tailor relevant ads to my interests. It seems like an ideal application of AI to me. They could do a far better job than serving me the lowest common denominator stuff they keep throwing at me. My Twitter news feed these days: * 10% - posts of interest from people I follow, i.e. the stuff I actually go to Twitter for * 90% - Ads and recommendations of topics to follow that I have zero interest in If it's going to insist on showing ads and recommending topics of interest, you'd think those could be better personalized, given that Twitter has years of my tweet, reply, and like history to train its AI on. But no... what I get is crypto ads, Hollywood events, celebrity news, sports news, etc.

jewayne 4y ago
You understand that you're not the one paying for the service, so you don't get a say, right? Even once The Machine has perfect knowledge of you, the ads will not be tailored to your preferences. It will be what an advertiser has paid The Machine to show you.
- arbuge 4y ago
  Actually I do get a say... take the Twitter example. In that case it's resulted in me visiting Twitter far less than I used to. I may not be voting with my dollars as a user here, but I am voting with my attention span. Dollars are not the only asset of value in play.
- cratermoon 4y ago
  > what an advertiser has paid The Machine to show you. As Cory Doctorow has point out so eloquently in his "enshittification" series, the end point isn't even to the benefit of the advertiser. At the end state, The Machine also have perfect knowledge of the advertiser, and the adtech company can turn the full power of the The Machine to the benefit of itself, extracting value from both the audience and the advertisers.
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santoshalper 4y ago

When I was starting out my career, my father gave me a piece of advice that has turned out to be incredibly applicable and valuable - "Stay close to the money". What he meant by that is work in environments where the work you perform is very closely tied to the revenue of the company - or another way of saying it - "Do work that is directly part of the organization's mission". It sounds like you've applied this to your career with good results.

mtnGoat 4y ago
Yup, always try to work in the profit centers, not the cost centers. Was reminded of that a job or two ago in a big way.
ludicity 4y ago
Every time I don't listen to Patrick McKenzie, I am filled with regret.

im_down_w_otp 4y ago

As a non-ad tech example, a significant component to what we're trying to do at https://www.auxon.io https://www.auxon.io is provide tech to companies they can use for testing & analysis of robots and cyber-physical systems. From our perspective internally what we're getting is the ability to perform something akin to "materials science" for the increasingly critical software parts of these systems and construct models of their behavior to use for predictive & comparative analysis purposes. We have a research partnership with the University of Ottawa & University of Luxembourg specifically on the statistical analysis end of things to go deep in some areas to later incorporate the findings into our products. In fact the first go around of that research cycle has already happened (https://arxiv.org/abs/2301.13807v1 https://arxiv.org/abs/2301.13807v1) and the insights are being integrated into our Deviant product (https://auxon.io/products/deviant https://auxon.io/products/deviant). It's definitely not ad-tech. It definitely has a specific applied use case. Most of our marketplace traction is in aerospace, energy, automotive, and defense. We're not immediately hiring for roles on the data analysis end of things (we're in much more immediate need of visualization & frontend help), but we will be this year.

magicalhippo 4y ago

> a clear and immediate impact within the organization This reminded me of those Microsoft Viva emails I keep getting. Anyone finding those useful for anything at all? To me it seems like a solution in search of a problem.

paavope 4y ago
That definitely goes to the category of products that exist to keep a highly paid data/development team busy

sizzle 4y ago

Please come to the field of computational biology, we need your help finding molecules that cure cancer and other diseases

plaidfuji 4y ago

> My heuristic when evaluating potential jobs is only to consider positions where the output of statistical analysis and machine learning models has a clear and immediate impact within the organization, while avoiding adtech for ethical reasons and because of concerns toxic work could enable a toxic environment. Currently starting to look for a new role and couldn’t have articulated my goal more clearly. The problem is that this narrows the field considerably, to finance/insurance/fraud (“pure money work”), healthcare/EMR (which as far as I can tell is also actually just profit optimization work for hospitals), and then you have manufacturing (closer to where I currently am), but the actual applications of data science are more limited and data Eng / analytics are more valuable, bioinformatics (which is really interesting, but also a large learning curve), and then just traditional BI (kind of generic and boring). Also, if you’re thinking about long term career development, many of these functions fall under the IT/CIO umbrella as orgs grow, which means that career advancement would require learning cloud architecture, cybersecurity, IAM, networking, etc. which I’m not saying is a bad thing, just an observation. Just applying statistics and modeling can only rise so high in an org, unless like you said, their core business and value prop is being the best at modeling some phenomena.

skrtskrt 4y ago
anti-fraud is very unsexy work IMO, but as long as you believe that (insert some internet-enabled service) should exist, then it can't be safe and enjoyable to use without serious fraud mitigation and prevention work, you can be secure in knowing that you're making life a lot easier for people who otherwise would have suffered from that fraud
- bglazer 4y ago
  Curious why you think antifraud is “unsexy”? I worked in the field for a few years. My experience was that the bulk of fraud is easily detected with bog standard models (most people doing fraud are lazy). But, there’s a long tail of super sophisticated work for detecting more subtle operators. It’s also pretty fun to learn about the convoluted schemes people come up with for doing fraud and to imagine how someone would attack a new service. Also theres quite a bit of money sloshing around and it’s relatively easy to quantify financial benefit from fraud reduction, so the departments tend to be well funded

ludicity 4y ago

I agree they do exist, and this heuristic sounds sensible to me. It's the good old patio11 "go work in a profit center" situation, and I wish I had done so. I'd probably be grappling with a new existential concern, but that's life (I wish I had a job that didn't matter so I wouldn't have to worry about performance all day!).

pfalke 4y ago

Well said! To add a few options - energy management (shifting loads to times when energy is cheap) for consumer/commercial/industrial use cases - energy markets, especially power trading: often highly algorithmic, and driven by models that turn fundamentals data (weather, calendar, …) into supply/demand predictions, and from there into price predictions - retail pricing, both offline and e-commerce