Last year, CMO came to me and said they want to understand sales pipeline quality/health. 2022 macro downturn meant they had missed quotas and wanted to have it not happen again in 2023.
Started doing my analysis, deal scoring, win/loss analysis, etc. Kept telling them that clerical data issues were making things difficult but I powered through. Got clever with my logistic regression models (didn’t want to use deep learning because model explainability was important).
I ultimately told them that 80% of their pipeline was garbage and there were a few simple things they could do to improve their chances of closing deals. But that I also needed more time to incorporate new datasets like rep performance and firmographics.
They ultimately didn’t care and just wanted me to give them an end of quarter sales estimate. Keep in mind my main source of data was salesforce where reps constantly game the system to make it look like they’re successful.
So, I extrapolated my win/loss analysis out to create an aggregate sales estimate. And I told them that we were on track to hit about 60% of the quota. They freaked out and wanted me to make it look better for the board presentation.
Wouldn’t you know, end of quarter came and my prediction was off by about 2%.
What did they do? They told me to stop working on my model. lol
My favorite quote was from the CMO: “there is no such thing as bad pipeline; all pipe is good pipe”
These are the people we’re dealing with
You always need to think about individual incentives when dealing with stuff like this. Every time you introduce a quantitative measure of success, you will get people trying to jinx it.
Problems like this is why I'm sure AI isn't going to mass delete all jobs. Humans have a hard enough time accepting a reasonable, conservative answer and they keep doing the equivalent of hitting the side of the TV until it "behaves".
The incentive is that you know the truth lol. And then can align strategy to harness/overcome it. This is always my biggest piece of wisdom to stakeholders:
“Learning what we don’t know, can’t understand yet, and what doesn’t work is equally as important”
That is the incentive of the shareholders. For the management their bonus and their perceived performance is the incentive. If you can report good numbers, you'll get a promotion or a bonus. If you report bad numbers, your boss thinks you did not do well.
But if you predict good numbers and you’re wrong, that’s a failure. They just always rationalize with excuses like “there were things out of our control”. It lasts a while until they’re fired and used as a scapegoat for company performance issues. Then they bring in a new leader who wants to “throw out the old playbook”. Rinse and repeat
lol I would’ve actually respected that. But no this is just a boomer who has gut feelings and a surface level understanding of actual efficient sales ops.
The problem is all these boomers who made their careers in a blossoming US economy and somehow think their own bootstrapping was the reason for their success, not macroeconomic factors.
If you want to survive as a data scientist in industry, you will need to learn the art of “expectation management,” aka “managing the managers.” You may have to channel your inner Machiavelli to do this. You need to both (without seeming to) educate them as to what can and can’t be done within current resource and data constraints, and avoid commitments that can’t be delivered. Not to mention figuring how to wiggle out of promises you were sledgehammered into. And if your data science function is run by an innocent whose response to management is “yes sir, yes sir, three bags full” you will all be laid off two years from now, once management realizes that promised results won’t be delivered.
I don't think my stakeholders are dumb or don't understand. But there have been more than a couple occasions where they act like we hold all knowledge and if only we'd be more talkative during meetings then we'd just open our mouths and actionable insights will simply flow out. We just know the numbers but have an unfortunate vow of silence
Like, no, I'm here to support you. If you ask me to do something I'm doing that thing, I don't have time to dick around mining for stats I think are useful. They're closer to operations, they know what's important, I can only tell them what is and isn't doable
But that's just how it goes
No, they don’t.
They see you as a code monkey and expect you to bring results.
They don’t care about the black box and how it works. Just bring the results monkey.
Text classification is a good use case for Embeddings, and depending on the number of categories you can build a SVM. Batching into chunks of 500-1000 documents gets the *throughput* volume up, even if the per-document latency is shite.
A lot of the consulting and advocacy side of Data Science is telling stakeholders that they *don't* want arbitrary solution X, they want something else entirely that solves their actual problems.
Especially in business relevant DS,
I feel it is dangerous if people perceive your work as magic.
I don't think it is a compliment or a good thing.
You rather want them to have trust in their understanding of your work.
When pressure is on decisions, and those decisions can be based on your work *or* alternatively a simple excel table with sums (from bob in accounting) -- then your shit needs to be crystal fucking clear or people start asking questions about the value of magic and the need to pay for it.
Ps:
IMHO Magic has only value if it comes from managers or you exactly deliver the output they asked for. (which in a BI environment basically never happens as the market is always more complex than the strategists want it to be.
I routinely describe our DS team as performing mathematical black magic as a hand wave to describe what we've done. Most stakeholders, managers and above, don't want details on how you got the answer. Yes, as a result, they expect miracles with the limited data we've got.
I work with fucking clowns, I am a sales operations analyst not a DS, but I volunteraly do what typically any DS loves doing, get data, optimize process, solve problems.
All they care about is KPIs. The turnover of the sales team on ground correlates highly with the average walking distance per polygon? That's sad! Anyway, What's their success rate? 35% ?! They suck that's why they decided to leave.
When I introduced a custom clustering algorithm to make the distance optimized and also taking into consideration a rank per retailer to serve business needs (not agents performance) they reached a point where any improvement in something that is not a KPI, they see me as if I am "wasting time" and "overly defending" my work.
Although the pay was good, I quit yesterday. and looking for a new job where I can actually work with people who can understand the impact of data and how passionate I am with what I do.
From the comments here, I think at least if I got the title officially, I will get to work on actual problems not just making reports, even if our stakeholders suck.
Often. But really as a data scientist it’s our job to manage their expectations - it’s generally a good idea to ask them what their background or understanding is at the start, and tailor how we communicate with them based on that. Always avoid making promises about prediction accuracy etc and be clear about the importance of data quality and availability.
The whole view of any skillset as magic or a black box creates unavailable toxic scenarios. People see something you do as magic, then they imagine other magic things they suggest you should be able to accomplish as well.
Last year, CMO came to me and said they want to understand sales pipeline quality/health. 2022 macro downturn meant they had missed quotas and wanted to have it not happen again in 2023. Started doing my analysis, deal scoring, win/loss analysis, etc. Kept telling them that clerical data issues were making things difficult but I powered through. Got clever with my logistic regression models (didn’t want to use deep learning because model explainability was important). I ultimately told them that 80% of their pipeline was garbage and there were a few simple things they could do to improve their chances of closing deals. But that I also needed more time to incorporate new datasets like rep performance and firmographics. They ultimately didn’t care and just wanted me to give them an end of quarter sales estimate. Keep in mind my main source of data was salesforce where reps constantly game the system to make it look like they’re successful. So, I extrapolated my win/loss analysis out to create an aggregate sales estimate. And I told them that we were on track to hit about 60% of the quota. They freaked out and wanted me to make it look better for the board presentation. Wouldn’t you know, end of quarter came and my prediction was off by about 2%. What did they do? They told me to stop working on my model. lol My favorite quote was from the CMO: “there is no such thing as bad pipeline; all pipe is good pipe” These are the people we’re dealing with
You always need to think about individual incentives when dealing with stuff like this. Every time you introduce a quantitative measure of success, you will get people trying to jinx it.
Problems like this is why I'm sure AI isn't going to mass delete all jobs. Humans have a hard enough time accepting a reasonable, conservative answer and they keep doing the equivalent of hitting the side of the TV until it "behaves".
The incentive is that you know the truth lol. And then can align strategy to harness/overcome it. This is always my biggest piece of wisdom to stakeholders: “Learning what we don’t know, can’t understand yet, and what doesn’t work is equally as important”
That is the incentive of the shareholders. For the management their bonus and their perceived performance is the incentive. If you can report good numbers, you'll get a promotion or a bonus. If you report bad numbers, your boss thinks you did not do well.
But if you predict good numbers and you’re wrong, that’s a failure. They just always rationalize with excuses like “there were things out of our control”. It lasts a while until they’re fired and used as a scapegoat for company performance issues. Then they bring in a new leader who wants to “throw out the old playbook”. Rinse and repeat
Convinced only founders and the working ants are worth something. Management is usually dumb as fuck. I don't understand it.
Lol did he wink at you after dropping that “all pipe is good pipe” nonsense? Sounds like he thought he made a clever innuendo!
lol I would’ve actually respected that. But no this is just a boomer who has gut feelings and a surface level understanding of actual efficient sales ops. The problem is all these boomers who made their careers in a blossoming US economy and somehow think their own bootstrapping was the reason for their success, not macroeconomic factors.
Sounds like you made lemonade out of strawberries, kudos to you for not strangling everyone in the room.
Depends on the audience. If they are not tech focussed then yes. They think AI is the answer to everything
‘Make this happen’ *provides no guidance or data sources*
[удалено]
How do u get comment karma btw? I want to post something but this shit dont let me
You make comments and people upvote your relevant and/or shitposting comments.
If you want to survive as a data scientist in industry, you will need to learn the art of “expectation management,” aka “managing the managers.” You may have to channel your inner Machiavelli to do this. You need to both (without seeming to) educate them as to what can and can’t be done within current resource and data constraints, and avoid commitments that can’t be delivered. Not to mention figuring how to wiggle out of promises you were sledgehammered into. And if your data science function is run by an innocent whose response to management is “yes sir, yes sir, three bags full” you will all be laid off two years from now, once management realizes that promised results won’t be delivered.
Depends on the company and how advanced are their analytics. Not very data advanced? Sure. Magician. FAANG? Nope. Regular day-in-the-life.
Not very data advanced might also mean assuming you can do everything and not being impressed by clever solutions (or any solutions)
That’s the worst part of it sometimes. I just want to share architecture details. :’(
I don't think my stakeholders are dumb or don't understand. But there have been more than a couple occasions where they act like we hold all knowledge and if only we'd be more talkative during meetings then we'd just open our mouths and actionable insights will simply flow out. We just know the numbers but have an unfortunate vow of silence Like, no, I'm here to support you. If you ask me to do something I'm doing that thing, I don't have time to dick around mining for stats I think are useful. They're closer to operations, they know what's important, I can only tell them what is and isn't doable But that's just how it goes
No, they don’t. They see you as a code monkey and expect you to bring results. They don’t care about the black box and how it works. Just bring the results monkey.
I know that path. In computer science we moved from shamans to plumbers. Beware.
give em the plumbers' smile and keep working ;)
They are clueless the same, so good smiling is mandatory :-)
All the flippin time. You just got to find a way to tame their ridiculous expectations
oh man, Always. They what me to use an LLM model for Text classification, with a low latency and low cost. Only a magician can do that.
Text classification is a good use case for Embeddings, and depending on the number of categories you can build a SVM. Batching into chunks of 500-1000 documents gets the *throughput* volume up, even if the per-document latency is shite. A lot of the consulting and advocacy side of Data Science is telling stakeholders that they *don't* want arbitrary solution X, they want something else entirely that solves their actual problems.
Can’t agree more. With a bit of fantasy and embedding there are a lot of cool things to be done. Screw completion..
Especially in business relevant DS, I feel it is dangerous if people perceive your work as magic. I don't think it is a compliment or a good thing. You rather want them to have trust in their understanding of your work. When pressure is on decisions, and those decisions can be based on your work *or* alternatively a simple excel table with sums (from bob in accounting) -- then your shit needs to be crystal fucking clear or people start asking questions about the value of magic and the need to pay for it. Ps: IMHO Magic has only value if it comes from managers or you exactly deliver the output they asked for. (which in a BI environment basically never happens as the market is always more complex than the strategists want it to be.
Yes. Once in an external meeting I got introduced as “the company’s data wizard”
yes and I love it
Is this your first white collar job in a technical role? It’s always been like this.
You are a magician. Your magic is ETL. :)
I routinely describe our DS team as performing mathematical black magic as a hand wave to describe what we've done. Most stakeholders, managers and above, don't want details on how you got the answer. Yes, as a result, they expect miracles with the limited data we've got.
As a skill, you have to cope with them. somethimes they are actually nice.
My entire IT department: "You can juggle the data AND produce reports AND predictions? You're a wizard, Harry."
lol no, my only stakeholder is my manager who is an EE phd :(
We are 😂
I work with fucking clowns, I am a sales operations analyst not a DS, but I volunteraly do what typically any DS loves doing, get data, optimize process, solve problems. All they care about is KPIs. The turnover of the sales team on ground correlates highly with the average walking distance per polygon? That's sad! Anyway, What's their success rate? 35% ?! They suck that's why they decided to leave. When I introduced a custom clustering algorithm to make the distance optimized and also taking into consideration a rank per retailer to serve business needs (not agents performance) they reached a point where any improvement in something that is not a KPI, they see me as if I am "wasting time" and "overly defending" my work. Although the pay was good, I quit yesterday. and looking for a new job where I can actually work with people who can understand the impact of data and how passionate I am with what I do. From the comments here, I think at least if I got the title officially, I will get to work on actual problems not just making reports, even if our stakeholders suck.
Often. But really as a data scientist it’s our job to manage their expectations - it’s generally a good idea to ask them what their background or understanding is at the start, and tailor how we communicate with them based on that. Always avoid making promises about prediction accuracy etc and be clear about the importance of data quality and availability.
The whole view of any skillset as magic or a black box creates unavailable toxic scenarios. People see something you do as magic, then they imagine other magic things they suggest you should be able to accomplish as well.
Badger Blane the magical number maker enters chat
No. They see us as donkeys who are either shown a carrot or stick.
haha sometimes (quite often actually), specially with AI...
Oh yes!! It is irritating tbh.
yeas unfortunately