OK, so welcome, Maria Kwacha and Maria, I'm going to hand over to you.
And you can take it from there. We're going to hear all about the day in a day in the life of a statistics consultant.
Yeah, yeah. Yeah. Big.
Thank you very much. So guys, very mind that we are in the same room sitting at the same desk, so the logistic may be awkward.
They're with us, please. OK, I'm going to show a scene.
Yes. OK. And you all seem to say this full screen is everything, not excellent.
Thank you, sir. Well, thank you so much for having us. I am Maria Christodoulou.
This is my colleague, Megan. What's going on? We wanted to talk to you a little bit about things like this.
Consulting on how we planning on doing this will give you a brief introduction of who we are in the consultancy and also where we are.
For those who don't know what we do in general and who we work with, and then I,
we will give you an overview of what our day to day existence looks like and some examples.
And then we'll tell you the things we really love about itself because we're both quite enjoy our jobs.
Then we're going to own up to the things we're not very good at.
I'm tell you the things we really struggle with, and we're going to finish in telling you how to.
You can get involved with the consultancy. Obviously, interrupt me if you have any questions, any comments,
there's going to be a part that I'm hoping it's going to be interactive, so you're going to hate me for it.
And those of you who are brave enough to pull your covers up on the two of you that says, I may pick on you, so.
Excellent, so the. The consultancy is composed of three members at the moment, we have chromite Scott, who is the director of the consultancy,
and she has extensive experience of almost two decades of working statistical consulting.
She's calling you maternity leave, but we were waiting to see her back in June of leave, beginning of June.
My colleague Mandy Ignacio Sosoli is a statistician and has quite a lot of expertise in all things that have to do with statistics, also statistics.
He thought a lot of things over time and I am a little bit Paul, but I wear multiple hats some of time by myself to the other half of my time.
I am a consultant and my expertise is mostly in biostatistics.
So what do we do, what we do in general is we help on absolutely every aspect of our clients work.
First of all, can I help the experiment the participants on so I can see somebody is giving me a blanket?
Thank you. Thank you. So we can get involved in study design.
So if somebody has a scientific question and the one to design an experiment, we can help them with the experimental design.
We can give them advice on data collection. We obviously don't collect data for them,
but we can guide them into doing it in a way that would give them the best chance of getting a robust statistical result.
Often we have to step in with data management of data issues or reformatting the data so that it can be in a format that they can use for modelling.
But the majority of our work is actually on data analysis, we handle data.
We work with people's problems and we try to get answers to specific hypotheses that they have formed,
and we write reports that they can then use in their in the publications for grant applications.
All the things we do review advice and public insurance that's very prominent in the pro bono work we do,
which many have not said we'll talk you through. And finally,
but that's quite close to my heart because I know the majority of it we do training and
that's statistics training for specific statisticians that need to use statistics.
It's we understand that there is an issue between people using statistics poorly.
But unless you go out of your way to train people to do this, rightly,
there's very little that can be done to improve the situation, and we're working quite a lot on that.
OK, I'm going to close the window again, guys, so you can see on the diagram right here.
So basically what we take care of face the entire consultancy cycle as you can see,
like we find clients and projects, so it can be that maybe we advertise ourselves,
which is something that we are planning to do like in social media channel,
for example, or that I had some clients recommend us to someone in their network.
Or it can be someone from our personal network who get to us and asks for statistical support.
Then what do we do? We try to really listen and and to clients formulate their problems because sometimes they do have a problem,
but they don't know how to actually seek out, you know, what is the specific point in which they actually need help?
Then often we have kind of done this requirement analysis.
This escalated this way we design and agree and the scope of work.
So what is the scope of work? Will detailed what quite a needle like.
You know, it's just below the area of the city civil service and we are going to provide and we cannot like a few details about, for example,
the modesty we are going to implement for them or if we're going to do some data cleaning and we can
discuss about this with the client before we start the work and we wait for the client to sign off.
Then we implement the work that we had sort of established in the scope of work.
As Maria said, we can take care from the design up to the quality assurance of the results to the client had already.
And so after we have implemented our work, we have done our analysis.
We are prepared to report to them.
We communicate findings to the clients just to make sure that we understand what we are the result of of our analyses.
Sandy, we have done within depredation in this part of time.
And then when we have the client sign off of the last deliverable added,
we have provided them we do a sort of internal evaluation of how the project went to where we can kind of do better or we ask clients for feedback,
actually. And it's something that we want to do in a sort of more systematic way.
Yeah, we want to ask all the clients about this, about the feedback they can have regarding our work.
And in this kind of cycle, there are a lot of activities that come on the side like we talked about on budgeting.
So we need to take care of we need to understand which is that for us to adapt to certain project requires
because we need to tell the client how many hours we will spend on that and how much it would cost for them.
Then we take care of all the stakeholder relations, so we try to communicate and engage with the clients and also like with women
internally to the Department of Statistics or another company we are working with,
which is Oxford University innovation, which absence with the contextual side of our project.
So we kind of keep the communication alive.
And of course, it requires quite a good dose of team management and project management because we are not a big team,
but also, like in all, trying to make two people work well together and make sure that everything is done timely.
It's quite challenging. So why didn't we invest a lot of time in organising the work?
And then, of course, we did have the communication that it's something we are still developing,
as it was saying, because we don't really have a proper social media presence.
But yeah, it's something that we are working on and hopefully in the near future, you will hear from us.
So who are our clients? We kind of grouped them into main categories, we have internal clients meaning internal to the University of Oxford,
and we have sort of classified ad hoc internal projects and pro-bono adult care represented
by researchers or even field students who come to us with a very specific research question.
And so we provide some help to them. Then we have like internal projects, and in this case, it's mainly research groups.
They say across the university so they can come to us at the very beginning of their project, for example,
when they are writing grant applications because they need, for example, help with the sample size computation.
Or they can get back to us when you know they are more in their analytical part of the project.
And so they need us to actually perform the analysis for them. Then we have the pro bono that Maria mentioned earlier,
and it's what really makes us so proud of the work we do because we are nine clients at Administrative Department of the University,
and we really try to ask them to improve their general level of statistics across the university.
And, for example, a client we have like.
They are exam companies. So everyone who is external from the University of Oxford, so companies are Universities International Organisation,
and on this side, we work with all UI that I mentioned a few minutes ago, and they take care of the contractual side of our work.
So they we ensure that everything is done properly, fairly transparently and so on.
And then as you can see, we have the teaching,
which is sort of just so because we can provide teaching both within the University of Oxford or to external organisation.
And we have just a very recently like yesterday yesterday signed a contract with with
an institution external to to the university to provide some specific training.
So my day in the life, OK,
we don't really have specific like a typical day because it very much depends on the projects you are working on our workload in good day.
So we work on two or three production simultaneously in that day, so it can be even more.
And of course, like each project, since we work, not quite they use of disciplines.
Each project come with its own challenges.
And so it requires a bit of time also to maybe get acquainted with the language sometimes.
And then you can tackle this statistical problem at times.
And of course, Maria and I would work very well because, you know,
we we have different strengths that are useful for different stages of our project management cycle.
And so, you know, we try to we have the content to make it so that you can tell the world that.
So what happens when a query comes in on the first thing that happens, it's normally an email on some of these asking for specific question.
We actually try to understand the general topic, and that would give us an idea of whether we have the vicinity of skills we need.
Do we have the skills that we need or do we have absolutely no knowledge of it?
And sometimes it can be a low skill from the first contact we don't really know.
Other times, it's something very familiar.
A few days ago received a query that these essentially what I did for my Ph.D., so we have more expertise in some things than other.
Now, if we find that we don't have all the expertise we need, we still can work out whether we can get the necessary skills for the task.
In a sense, it's a question of is this something that I can actually quickly learn to implement because I know
sufficient statistics on my own to be able to understand the new method that's relevant to this.
Then we try to get some clarification, professional timelines, for example, to very important, can we fit it into a workload?
Our workload is quite varied in terms of what we have to do and when we have to do the things on there.
Some weeks that are much more difficult to include new things in than others.
But also we need to take into consideration when our clients will need delivery of the final output we're going to give them.
Is this going to be too late or are we doing it at the right time for them?
Normally we follow up by requesting a meeting with we have found that the timelines look OK.
We we feel like we can provide some help for this. We actually request a meeting to get more in-depth understanding of the problem.
On that meeting, we need to verify that we really are happy, we understand exactly what they need and that we're comfortable putting the query.
In some cases, it may be something that's beyond our expertise, and we wouldn't be able to do a good job on it.
So we need to run a quality assessment of what we will be able to provide, need to be very critical and harsh with ourselves.
But we need to be able to ensure that everything we present is of the song that we want it to be, which is why time?
One of thing is we it's about stage where we normally have a discussion in case of something that concerns us in terms of ethics, for example.
That doesn't happen often, but some projects they want to.
And that's often external projects, not in time, but they want to use findings in a particular way that makes us uncomfortable.
And for that, we use our judgement and our own moral compasses and we make a decision on that.
What we do have to that is we put together are all the money.
You already said that we put our scope of work and that's exactly it.
Essentially, we make a document that says these are the things that we're going to be doing to answer the question, but our clients want to answer.
This is when we're going to be doing it and this is how much it's going to cost and we pass it on to our clients for an agreement.
If it's an external company that then has to go through a UI for a formal contractual agreement.
And this is what a query looks like.
This is a fictional example, because this was the one that was given to me at my interview when I was interviewing my current job.
This is what they look like. Yes, I think that's great. This is what they normally look like.
It doesn't affect what they're saying.
So it's like they come with a certain set of questions, and we need to quickly understand whether a this is something that we would probably help.
And B, we need to decide what questions would we ask them in the follow up meeting to be able to really get
enough information for us to make a scope of work and be able to carry on with our work for it?
So this is the interactive part, I'm afraid, so feel free to either use the chat if you want in.
Sorry, let's go plug. It's no free to use the chat and.
And give up on that or unmute yourself. But what questions would you ask in a meeting if you went to see this particular query?
What are the things that stand out to you? I want to be a consultant.
Thank you, Garrett. We are being recorded. So that was a question I got very excited.
Anybody brave enough, so quiet. Point one isn't exactly clear.
Perfect, why isn't it what what makes it unclear? It it doesn't really read as English.
But it's a bit out of context. Yeah, I was going to say, I feel I feel like my immediate thought is,
can can we get a little bit more context about exactly what they're trying to achieve there?
I mean, the thing you started talking when I was typing, the next thing I was going to type,
which is the thing that's immediately jumping out here since you mentioned ethics previously,
is they're talking about working with adolescents and school pupils.
So in addition to sort of all of the usual ethics that goes with collecting people's data, you have you suddenly have enormous safeguarding issues.
And so it's of additional ethical issues surrounding it. Are you are you going to be directly interacting with students?
Are you going to be saying, you know, keep data on people under the age of 18?
I'm sure there's a whole extra level of security that you have to go through there to make sure what you're doing is actually ethical.
So above and beyond what you would normally do, just dealing with people's data.
The topic is my sensitive is not just the fact that we have, in some cases, young adults or teenagers.
It's the fact that we're looking at mental health, which could be it's this this could be vulnerable adults,
but it's also protected characteristics that will be collected.
So should this project be funded, we would have to see that they have a threat, for example, because that's an internal project.
We have to see that they have passed an ethics bill to be able to do this and that we have been included.
If we are to have a handle the data, we have been included and there have been specific, so specific.
Up specific ways where we can access the data without compromising anybody's personal details, even if they are anonymous today.
We still need to protect them, respect them. The other thing is that.
It's very difficult to actually understand what kind of analysis this would mean.
This is quite close to your expertise in terms of models, so when you saw a query like that,
what would be the actual statistical questions you would ask yourself?
Well, first of all, I think that's what Fergus was saying. It's really like it's a bit out of context.
So one of the first questions they would ask would be like, Can can I have more information about your project?
And in general, when you let them speak,
you can kind of pick up elements that you know are out for you to understand which kind of statistical model then you can,
which kind of statistical problem they may have in order to answer to this specific question.
And in particular, what they find useful sometimes is actually, I kind of process what they're telling me, what they're saying to me now.
So you meant that, for example, wanted to you have a radius scale, which is measuring these properties for adults.
So you want to be that's what adolescents do. You have any other, for example, example when they're doing what they're doing that?
Can you find an example, you teacher, that you should have the same measures can be used for both adults and children when I got this sense.
And yeah, in this case is a very specific question because it's up psychometric scale.
So it requires very specific kind of analysis that need to be done.
So you want, for example, one thing that you want to measure is are the items in question of actually measuring what I want to measure.
So you have like a sort of latent construct that is correlating with these items and you need to kind of understand this.
It's exactly what thereafter is exactly what it is doing.
And then of course, there are a lot of other kind of you can go more or less enough see depends on,
let's say, the first meeting with the client that you're going to have after having received it.
But this is just to give you an idea of which the kind of queries we receive this is the thing.
The thing with these things is that quite often for interactions, the majority of what we need to do is we need to use soft skills to get answers to.
Statistical questions were not been asked directly because if I spoke to somebody in a sciences and I start
asking questions about effect size where there may not have considered it does affect size in that sense.
I may get a confusing answer if I start asking them, Is anyone else found this on what were the sort of numbers that we're getting from them?
So you need to often asking direct questions to get very clear answers because you don't also don't want to be the main statistician on the call.
That's terrifying everyone. You want them to relax and tell you about the project and force you with the actual results.
But this is this is what it looks like. Authoritative time on. This is what we try to do.
We look at this and say the first thing I would say is, OK, this needs we need to see if that has a wreck, as is how the fix is responded.
What is the actual context of this project? And then we're thinking this is psychometric scales.
We need to know whether they have,
whether the property is basically that they're trying to actually estimate and whether they have been properly calibrated in that sense.
Let's say that the first reaction is like. Well, what is the then we have what kind of reaction that hype?
So what do we do like how do we deal with that?
We received questions like email. We say, what? Then we try to read it and understand what can be the questions.
And then we want to ask the clients because we do have quite a bit of preparation before that.
And in general, we kind of we really discuss a lot between each other.
We because maybe one of us can have can, not just something that the other person noticed.
And I think it's really valuable. And then as soon as we have agreed with the client clarified,
all our initial questions define what we are going to do to actually answer to their problem to tackle this problem.
Then we start working on that and we make a point of updating regularly to client and what we are doing because,
you know, sometimes you make estimates of what is the time that you need to perform to perform a task,
but then you realise that the data are messy, that you would expect change and the model is quite small,
is more challenging than you would expecting because there is some something little that really doesn't work.
And so, you know, you kind of keep on, keep them posted and, you know, so they know where you are.
They know if you need anything from them and you know how to count how you can move forward.
And in general, our output is it's a report that we can write to,
even like it can be very extensive or it can be just bullet points providing some insights and insights.
And we also provide the code, which in general is almost on file,
which we use to generate the report so they can find the code with what they need when they need it, and they can rerun the analysis.
We think it's a good practise because some it gives us a spot in Sichuan for work and they can go ask questions about that.
And it's also a way for providing a sort of training on specific maybe functions and comments or not.
So we wanted to give you just an overview of other few cases that we have.
We have worked on and we have divided them by kind of clients that we have, for example, the general case studies.
They come from two departments in the university. The first one is computational biology.
The other one is sociology.
In the first one that is actually came to us with a very specific research question, which was understanding the role of geography,
so the geographical location and the genetic data of an individual in their survival mechanism and in particular,
they were interested in how you can yield meaning all the elements belonging is coming from a population such as Neanderthals,
Denisovans, and they came to us with our with their database, which we have a few concerns about the quality level.
And so we we have to work around the data and try to find out which was the best subset to actually
respond to this question and be confident on the quality of the estimate we would have got.
And now, after quite a lot of working and communication and changes, we are in the stage of writing a paper with them.
So it was really rewarding as a project. And then the other one was an experiment from its a peculiar design of this experiment,
and they needed someone to ask them to find the optimal number of scenarios they had to
present to their respondents so they could have robust results out of their survey.
So this one, this is quite challenging. We're still working on that.
But you know, we're kind of progressing and this was an opportunity for us to learn a new technique.
So the client, like we did, have discussions in the beginning. We were very upfront with the client.
So we really value honesty, like saying, Look, I don't have the skills, but if you can provide training, we can absolutely help.
So they had someone in the research group who was working on that and actually at the moment was too busy to talk to us to keep on asking them.
So they provided training to us and we had the chance to learn something new, offered them a reduced fee, of course.
And then, you know, I'll give them the results they were waiting for. Then we had some similar case studies.
The first one is the university, the second one, these are medical. These are medical company based in Oxfordshire.
And in particular, the first one was quite interesting because the researcher came to us, but they didn't have a very specific research question.
So we asked them to looking at the data,
try to fine tune their research question they could have wanted to answer to or they may have found interesting.
And in particular, it was a very interesting project because I was about them analysing the genetics,
the generating process of errors in computer systems, assisted translation studies.
So it's not like. Something that you can find every day to work with,
and the other case was they had this medical company had created a new biomarker
and they use it just to diagnose together with some recognised biomarkers too.
They use it to diagnose certain conditions, and they wanted to see if there were any interesting options in their data.
So we have done with the analysis and received quite a good feedback from them, actually.
Then we have done teaching online that any person we like, as you can see outside this case is quite fabulous.
So one was the Department of Psychiatry. The other one was a CDC data science.
I was four with the depicted institute and it was really, really interesting.
So we had a different kind of audience because the first one was with people who didn't have a probability, much training.
So they didn't have a very sound, much background while the other one was with people who had a solid background.
Then it was sort of funny trying to tweak the content and, you know,
just select the audience and then they put a bomb on one just to give you an idea.
The first one is from the financial department of the university.
The academic activity surveys a very important survey, which comes in every every year,
and it's very important because the university asked to communicate the results to the UK government.
So they wanted to have an idea about the robustness and the quality of disability they were going to deliver.
I just to their to the academic staff. And we didn't find any kind of concern from us to the way in which this review was structured,
but we realised they didn't have a specific kind of they didn't.
They didn't have accommodation for some of the disabled staff, but they may be actually quite savvy.
We had sort of equality and diversity concerns, so we faced with the equality and diversity and inclusion of what we see of the university.
And then we worked with the alumni database, which and it brings together all the data from the alumni, from different colleges.
So you can imagine the quality of this data and the person who was working with that was really concerned.
So it again to us because of course, as you might imagine, they used this database of for funding reasons.
So it is a very important role, and it's really important that the quality of the data in the database is quite high.
So let's talk about the things we really love about our jobs, and there are quite a few of them.
But for both of us, the most important thing is it's so diverse, it's constantly changing.
We're learning new things all the time. And no two days with all the same.
We haven't had a project that is identical to something else. We'll just take the solution we had for someone on reapplied.
Every day is said to be something new and we are learning so much through this.
We also become better communicators, especially since we have to communicate quite some quite nuanced ideas and concepts,
but we have to do it in a way that is correct for the actual discipline.
For example, my agnostic you mentioned earlier that the first bit we may even have to learn the language of the discipline.
Multiple disciplines could be using a very similar technique, but they're calling it different ways.
Or they may have some standards that they follow, not just on paper, in any other discipline.
We have to limit them to be able to communicate with the researchers and understand what it is they want and whether we can provide it appropriately.
We also feel we make a difference in the university every time we have an ad hoc
query and there is a full student somewhere who is not sobbing next to the data.
That's a win. Every time we have a pro bono where we actually help make this, make this universe become more solid statistics,
that's a win for us every time we teach somebody to do something they didn't know how to do.
Again, a win. So we make a difference and we do meet wonderful people and we meet some exceptional researchers.
I it is really a joy. I'll find talking to joy.
And there are some things we don't love, and we don't love them because some of them are our fault and we admit to that.
Others, they just can't be helped. We do have the usual suspects.
Our workload is quite heavy and. As careful and those horses as we are with our scheduling,
we are at some point going to end up having the same deadline within two deadlines within the same hour,
and we're going to be panicking and there will be tears and that does happen.
It doesn't happen often, but it does happen. We also have competing demands,
sometimes we need to decide to put the project aside when we're making good progress in it because something else is more urgent.
And then when we pick up the project we put aside,
we have forgotten half of what we're doing and it needs extra time and it's a little bit frustrating.
One of the problems we have on that is down to us to get better if it is accurately estimating how long things will take,
those takes longer than we think, even when we think.
Who are the buffer we go over the buffer sometimes. That's because we don't often know what we're going to find until we get in there,
and we have already agreed contracts in all terms of reference, how long we think it's going to take before we have the chance to really.
Get properly inside the programme and try the modelling we have.
The only time we know how long it's going to take after Italy is after we've invested pretty much half the time we estimated for it.
We also try to do too much in limited time, that's probably personality based,
we have both have tendencies for perfectionism, which means we also overthink and over check.
You mentioned the project of the values and that is a really wonderful project.
What they needed to do is they wanted to do a canonical Barrett's analysis.
That's analysis as everyone agrees that it should have one assumption of the East and that the assumption is multivariate normality.
So the data were all relatively low quality and they were not behaving at all.
So we start transforming and changing things around and trying to fix it.
But we ended up checking and doing different types of motivated normal tests,
and we ended up doing six different types of multivariate normality test because one just wouldn't do for some reason.
So what we need to learn is we need to learn how to control our tendency to overthink on over tech.
Because let's be honest, when we did six test,
we didn't get more information than if we had done just the one that told us there is nothing good here,
so we need to learn to be better with our own tendency to overdo it.
And the good thing on this is that you can actually kind of improve by time to time.
So, you know, now I feel much better on that. Did I used to feel, let's say, six months ago?
So yeah, it's a kind of steep learning curve, but it's exciting.
It's up. We have a bit of a buddy system that we try to set up where we tried to check each other's instincts when they're not particularly helpful.
So the final part of our talk is how you could actually get involved if you want to and now will just give you a brief overview,
and we're going to talk more specifically about things you can do,
but you can get involved with any part of our work that you're interested in teaching, including concerning actual consultancy.
You can get involved in all of this and you can be.
The sorting puts off a couple of hours or maybe two a day, or you could actually provide consultancy service as part of what we're doing,
which could be more than just a couple of hours you can teach with us.
You can call mine either be this will teach a specific field if you want to get an experience or
teaching or communicating with audiences that you may not be so familiar when teaching for the mean,
it's a different set of skills to meet. And you can help us by collaborating on satellite projects,
and that can be very intensive level of effort, it can be from a couple of days to quite a few days.
Yes, basically,
I wanted to add very quickly on what Maria said on the other slides that we have a system in place at the department to currently do so when
we receive a call that we may think like we think may be of interest for any of the details or annual academic stuff in the department.
We send a sort of call out to everyone, so you should all receive it.
We have not been sending many lately, but you know, we found that you should receive it and then it's open to anyone and the person, is there.
Someone who is interested can sort of contact us by email and then we have something put in place with the HRA.
So there is a proper sort of contract that has been signed between the consultancy and to give you.
And of course, it gives the chance to their student to earn some money.
And yes, for the for the time, you are certainly investing in that.
So it's something really to keep an eye out. And, you know, just see if maybe something that, as Maria was saying, is exactly your Ph.D.
So maybe just like minimum maths for maximum gain.
And all we can say that you are interested in acting out like you can send an email to us
personally or we have to statistical consultants email or you can come in the room 310.
We have a lot of good instances which can be so chocolate. Yeah.
And yeah, you can just let us know what your radius of expertise are.
So, you know, maybe we can we can kind of see if there's something that may be of interest for you.
Then we have some future plans that we will like to ask about you.
Like just have your opinion on that.
So we will have to provide some consultancy training in the department, maybe something like more extensive than the stock,
like a week in the life of a statistical consultant where we can sort of talk of the consultancy cycle,
explore every aspect and select do something together, like if we see a query from a client or we,
we can create a mock study and then we can work together on each stage.
And just as you gain these skills.
Another thing that we wanted to do, which is more like we are therefore supporting you back,
is more you are on your own so we can leave you in the wild, which is the statistical consultancy challenge.
So we would like to create like groups of feet for people and give to each Real-World statistical query like Consultancy Way.
And then, of course, you will have support from us.
So we would like to find you on the scope of work like, you know, try the questions that you want to ask to the client and to with analysis.
Maybe we always work on a peer review basis.
And it's always helpful so we can provide this for you and we can be there when you are having the meeting with the client.
And, you know, just ask you throughout the process and something that we wanted to do as well.
And this is sort of building blocks, lunchtime data, carpentry workshops.
So we would like to actually give everyone the skills to be able to.
Transform that data.
And like in the way that they would need to, and maybe they don't know to do that at the moment, so we thought they could be something an addition.
Maybe someone already knows that we could be still interesting swing by seeing if there's anything else they can learn or they can.
There's something new. And why would you want to get involved?
That's the question. Well, it's exciting. I mean, if you have not ever at to from there, the presentation is very much our fault.
But, you know, it's really exciting and you that learning curve is really steep,
but it's really rewarding because you actually learn something new almost every day.
And it's something that I personally really value like. Like, I'm building my toolkit and that it can apply to two different disciplines.
And it's really it's really interesting. I feel I've become a better researcher since starting from the consultancy because
I'm gaining knowledge in things that are actually relevant to my research.
I just wouldn't have gone so far out of my actual research area to find them.
So it actually helps with research as well.
And the I, as we were saying, the work experience so you can have a little income, which is that I was at the moment for intimate consultancies,
but externally, defensively, much of the client and what they need them for how long they would need someone.
And of course, another thing is that you develop a lot of soft skills because you collaborate with others so you don't to communicate with clients.
You learn how to communicate with your team members and just you make sure that everything runs smoothly and you do a lot of work on yourself as well.
As Maria was saying, I was saying earlier, you know, how to really say to like,
make sure that you don't overthink over check things and you are just like you get better every day defying deadlines, for example.
And yeah, that's all from us. So thank you very much for listening.
I was up Seamus Green, OK?