So, a synonym for 'consultant?' :)
I've always used them as "independent 3rd parties" who were listened to.
This has been going on for mellennia, unfortunately.
I want to make the move from development into consultancy and would appreciate hearing how you did it.
I cannot DM you, but if you have the time to type a small paragraph, I'm all ears.
If you don't want to make that public, you can directly email me (<HN-username>@gmail.com)
It was a LinkedIn recruiter message which I usually ignore. However, my SO did not (she is in IT as well) and convinced me to join a hiring event. I ended up liking it a lot and went through the hiring process. Soon, I started out on the most junior level and joined my first project with 3 very senior colleagues after a few weeks.
The learning curve was very steep both on the technical level and also regarding the consulting aspect - at first there was nothing I could 'consult' on due to lack of experience. This changed with growing experience, with the guidance of senior colleagues and my private efforts to gain skills and expertise.
Let me know if this was helpful!
Thanks a lot!
A few years ago there was a problem with storm-water infiltration into my (elderly at the time) mother's property from her neighbor. I, being a dutiful son and a civil engineer, investigated it and came up with the probable cause, the likely effects of non-action and the most cost-effective solution. I presented it to my mother in the most layman-like terms that I could. She said she'd think about it – meaning she'd refer i.e. defer – to her daughters. In the meantime I had a very layman-like chat with my mother's carer and told her the situation in layman's terms. The carer listened and said that what I said I made total sense to her. Later on, one of my sisters accosted me and stated that it was completely obvious what the problem and the solution was – "even the carer could see it". Human foreheads don't have the real estate for where my eyebrows wanted to ascend.
My advice is to consider whether the message should be separated from the messenger somehow.
Recent tech layoffs is the other side of that growth, when cheap money is gone and company is forced to stick to core competencies and shutdown growth plans
We are accepting a whole lot of assumptions every day.
The definition of rational is being informed enough to know what said chemicals will do in the short term and long term in order to make an informed decision, but then I'm reminded we dont get taught any of the above unless we specialise at a Uni, so most people cant make any sort of informed decision.
Had the same problem when I was an economics researcher -- publication bias for what stakeholders want to hear (often the government) is rampant because that's where funding for the economics department mostly comes from.
This is also known as The Iron Law of Institutions.
it isn't clear to me how the grounds for realizaing the theory are reconciliable with its conclusion
-- Robert A. Heinlein, Tunnel in the Sky, 1955
This is the only reason why a 60+ PowerPoint slide deck can cost several millions dollars
It's easier to poke holes in predictions of the future than in interpretations of the past, especially when the people making those decisions have likely reached their decision-making status through "how things have always been".
It took a lot of analysis and time to clarify to higher ups that we weren’t just P-hacking , but at least they were concerned about that.
> Data Scientist's job is to launder management's intuition using quantitative methods :)
It’s no different than the days when grey bearded wisemen would read the stars and weave a tale about the great glory that awaits the king if he proceeds with whatever he already wants to do.
The beards might be a bit shorter or nonexistent, but the story hasn’t changed.
I was the infra lead on a data lake project and got take part all the way to breaking down the data and turning into PowerBI reports. The result was "sell more" and to clients who marketing already identified, years ago, as whales.
There were some interesting other insights, esp. w/r/t to niche products that sold around weird dates (Easter, Memorial Day, 4th July -- but not obvious gift days like Valentines or X-Mas), but it led to a lot of "you're doing it wrong!" recriminations and follow up projects.
I would say it was a very engineering driven org however, so if you could present compelling data it could go a long way.
Also someone to blame if it doesn't work out
Nonetheless, the CTO went on a multi-year, 10s of millions of dollars, huge data tech stack & staffing reorg shake up... with really zero data points explaining the driver, or what we would measure to determine it was successful.
So it became a self referential decision that we are successful by doing what he decided, and we are doing it because he decided it.
Ouch. This is savage, but sadly correct in many cases.
HOWEVER, to play devil's advocate here, I've also seen corporate data scientists overstate the conclusions / generalizability of their analysis. I've also seen data scientists fall prey to believing that their analysis proves would should be done, rather than what is likely to happen.
The role of an executive or decision maker is to apply a normative lens to problems. The role of the data scientist / economist / whatever is to reduce the uncertainty that an action will have the desired effect.
Positive claims are about what is true. Normative claims are about what should be true, or rather what decisions we should make. Put another way, positive claims deal only with facts while normative claims deal also with values.
GP is saying that it's the data-scientist's job to give the executive the facts and it's the executive's job to decide what to do about the facts.
So often, those models proved to be over-fitted and not generalizable.
But too many decision makers simply can't properly judge such results.
Where do business analysts fit into this dichotomy? Their whole job is to poke around in Tableau in order to surface high-ROI strategies for the business to pursue. (Where, in choosing which proposals to surface to management, they're effectively making 90% of the strategic decisions.)
Or how about corporate buyers in trading and retail companies?
Or quantitative investment managers?
If understanding the data generating processes is the goal, I'd rather see some useful plots than wade through a technical description of some model whose assumptions were flagrantly violated.
The skepticism isn't a problem, the unequal application of it, the potential to harm careers, and the chilling effect as people wisen to how best meet their own personal goals is.
The SNAFU principle: communication is only possible between equals. When an hierarchical divide exists the subordinate will tell the superior what he wants to hear.
Sadly true. As humorously depicted here[0]:
In the beginning was the DEMO Project. And the Project was
without form. And darkness was upon the staff members
thereof. So they spake unto their Division Head, saying, "It
is a crock of shit, and it stinks."
And the Division Head spake unto his Department Head,
saying, "It is a crock of excrement and none may abide the
odor thereof." Now, the Department Head spake unto his
Directorate Head, saying, "It is a container of excrement,
and is very strong, such that none may abide before it." And
it came to pass that the Directorate Head spake unto
the Assistant Technical Director, saying, "It is a vessel of
fertilizer and none may abide by its strength."
And the assistant Technical Director spake thus unto the
Technical Director, saying, "It containeth that which aids
growth and it is very strong." And, Lo, the Technical
Director spake then unto the Captain, saying, "The powerful
new Project will help promote the growth of the
laboratories."
And the Captain looked down upon the Project, and He saw
that it was Good!
And then when I got old, I realized, there is even a reason that some people want status quo... because they have usually been around long enough to see society fall apart into anarchy and mass murder, so in their mind, they are doing the right thing.
- Galileo Galilei
A modern characterisation of "The Galileo Affair" would be that he was SWAT'ed by someone he was really really mean online to.
Thus the whole "Galileo affair" starts as a conflict initiated by a secular Aristotelian philosopher, who, unable to silence Galileo by philosophical arguments, uses religion to achieve his aim. [1]
While delle Colombe was almost alone in arguing publicly against Galileo, there was a group of scholars and churchmen who supported his Aristotelian views. After Galileo referred disparagingly to delle Colombe as 'pippione' ('pigeon'), his close friend the painter Lodovico Cigoli coined the nickname 'Lega del Pippione' ('The Pigeon League') for delle Colombe's group.[2]
The affair was complex since very early on Pope Urban VIII had been a patron to Galileo and had given him permission to publish on the Copernican theory .. this was very much a case of personal vendettas and internal politics rather than a straight up case of "The Church Versus Galileo".
[1] https://en.wikipedia.org/wiki/Galileo_affair#cite_note-Spell...
[2] https://en.wikipedia.org/wiki/Lodovico_delle_Colombe
[3] https://en.wikipedia.org/wiki/Dialogue_Concerning_the_Two_Ch...
>> Whenever my analysis contradicted - there was so much scrutiny in numbers, data quality, etc, and even after answering all questions and concerns - analysis would be tossed away as non-actionable/useless/etc.
It's a good sign at the company that I run, anytime our analysts/data scientists come up with metrics that say we're killing it, or that our ideas should bear a ton of fruit, the kneejerk reaction is to be extremely skeptical of the results. Usually they're still right.
When the data scientists say we're fucking something up, we tend to pay a lot more attention.
Only the paranoid survive, after all.
I found it incredibly stressful to discover and provide analyses (even experimental results) that wasn't expected, or contradicted prior beliefs. The findings were always very harshly scrutinized, and typically lead to tons of pointless extra work to 'understand what is going on'.
My friend runs a successful market research agency and she says she gets called in when management have decided they need to make a change but need evidence to sell it to the shareholders and staff.
That was a plot point in Dirk Gentley's Holistic Detective Agency (1989), though the observation much pre-dates this.
I did several experiments, and noticed that whenever I produced analysis that was in line with what management expected - my analysis was praised and widely disseminated. Nobody would even question data completeness, quality, whatever. They would pick some flashy metric like a percentage and run around with it.
Whenever my analysis contradicted - there was so much scrutiny in numbers, data quality, etc, and even after answering all questions and concerns - analysis would be tossed away as non-actionable/useless/etc.
if you want to succeed as a Data Scientist and be praised by management - you got to provide data analysis that supports managements ideas (however wrong or ineffective they might be).
Data Scientist's job is to launder management's intuition using quantitative methods :)