Meet Women In Data: Kirsty Garshong

Charlie Waterman our consultant managing the role
Posting date: 10/8/2020 10:58 AM
Slowly but surely, we have seen the gender gap across the Data Science sphere closing year on year. In fact, we were pleased to report that women make up 30 per cent of the industry in 2020, a large uptick of 5 per cent from 2019. 

Whilst we’re edging closer to that desirable 50/50 split of men and women, it’s hard to ignore the issues that persist in the gender gap. A large proportion of women in the industry are at entry-level, and this has a big bearing on the problem, resulting in a gender pay gap that is above the national average in the Data Science sector; for every £1 a man earns, a woman earns only 89p. 

Here at Harnham, we want to go further than just reporting on the state of play, we want to be catalysts for change. It’s all well and good that these damning statistics are reported on and brought to the attention of the public eye, but if we go no further than this, how can we expect change to happen?

We would like to welcome you to our newest series: "Meet Women in Data". A platform for incredible women in Data, and their male advocates, from across the globe to share their insights into the industry; its highs and lows, its challenges and wonders, and the steps we need to take to ensure diversity continues to be at the top of the priority list for the future generations of Data Scientists.

To kick things off, we spoke to Kirsty Garshong, Senior Manager for Contract Recruitment at Harnham. 

About Kirsty


Hi, I’m Kirsty, one of the Senior Managers here at Harnham, and I’ve been part of the UK contract team for six years, now heading up our Diversity Committee.  

I must admit, when I first joined the company, I thought the job was hard, and most days I really lacked confidence. I simply couldn’t get my head around the tech. However, as time passed, and as I spoke to the candidates I dealt with, as well as my colleagues and others within the industry, I began to make sense of it all. 

The struggle to grasp the technicalities wasn’t the only thing. I did feel my gender hindered me. A lot of the candidates I dealt with were men who were incredibly transactional and rather uninterested in what I had to say, something my male colleagues didn’t struggle with at all. 

It took a while to learn, but the only way to deal with this was to make it very clear as to what the benefits of speaking to me were, and I learned to meet abrasiveness with directness.

Looking back on those first months, I feel incredibly proud that I persevered. Knowing what I know now, acknowledging how underrepresented women are in the industry, to be one of the few who can speak up, speak out and make a difference is fantastic. And anyway, I ended up being quite good at the job!

How do you think the industry perceives women in the tech space?  


I think we’re like gold dust, especially in recent years. There’s not as many of us compared to our male counterparts, and we’re highly sought after.

This positive approach to women has most certainly changed for the better. Even in my six short years of working, we weren’t always seen as such an asset. A few years ago, women would have to fit around companies, not the other way around. We had to have the right goals, the ‘correct’ outlook on life and we had to benefit our superiors; there was no question of how companies could benefit us, or how they could support our personal and professional development.

How do you think those outside of the industry perceive women who work in tech?  


There’s certainly a stigma, not necessarily a negative one but you’re definitely seen as an exception to the rule. It’s widely known that the industry is male dominated and so, as a woman in tech, you’re expected to be a female who doesn’t have traditional female values, like wanting a family. Of course, this is inherently incorrect.

I know that this stereotype arises from the very nature of the job. We work in a demanding industry of long hours which requires an extremely high level of skill in order to succeed. So, if you were to take time out and not keep up with your learning, it’s highly likely that you could fall behind and not be able to fulfil the expected demands on return. 

But it’s not just women that take time out, men do too, so why is there the assumption that it’s only maternity leave that runs the risk of dropping a couple of balls? 

Ultimately, whether you’re a man or a woman who takes time out (for whatever reason) and don’t take personal responsibility to keep up with the ever-changing curve of tech, you’ll fall behind.

Do you, or have you, come against any issues in the sector because of your gender?  


I went to a meeting once with someone I managed - he was new, and we were meeting with a male stakeholder. Despite being said stakeholder’s only point of contact long before my junior colleague came on board, the stakeholder only addressed him. Any questions or topics of conversations, he simply wouldn’t regard me. 

When we sat down, I then explained who I was, and he looked very embarrassed. He didn’t acknowledge what he had done or apologise, but for the rest of the meeting he was very meek.

Currently, for every £1 a man earns, a woman earns 89p. What are your thoughts on this?  


I think it’s absolutely ridiculous. What can a man do that a woman cannot if facilitated in the right way? If I’m on a level playing field with a male counterpart, I should be paid the same. End of story.

Why do you think it’s important that there is a good representation of women in tech?  


A woman can do anything a man can do in tech. There’s nothing biological about the job, it’s all reliant on skill but unfortunately, there’s still this ingrained idea that tech is a man’s job. 

Despite this stereotype, some of the best data scientists were women, they quite literally changed the world. But without diversity, the number of girls looking to take-up and apply for STEM-based subjects at school and university will decline. Unfortunately, women won’t want to be trapped in an industry perceived as a ‘boys club’. 

We need to continue to work hard to inspire the younger female generation and create a balanced gender split across the whole industry, and that’s only going to happen if women are the face of data as much as men are. 

During your time within tech, has the gender conversation changed?


It’s become very much an expectation that employers have a mixed list of genders when it comes to the recruitment process. If I was to give a client a candidate list made up of just men, I am confident the client would push back on the lack of diversity.

Even just six years ago, this would not have been the case. In fact, more questions would probably have been asked if there were several women on the list around whether we had stressed the role would be in a highly pressured environment with long hours. 

There is still a very low number of women in tech. What more do you think could be done to change this?  


I think employers need to start to look inwards at themselves. Do they know what they’re do-ing to help diversify their business, do they know exactly what they’re doing to attract candidates and, most importantly, are they aware of what women want from an employer?

In Harnham’s most recent Diversity and Inclusion report, we found that the top five important working benefits for women are: the option to work from home, a bonus scheme, health insurance, enhanced pension contributions and an education or training allowance. Employers that don’t offer these benefits are discounting a large pool of female talent. 

It’s not always the case however that employers don’t offer these sought-after benefits, it’s that employers don’t bring awareness to them, so potential employees simply have no knowledge of them. For example, this year in the UK, only 22 per cent of employees knew about their company’s parental leave scheme.

We also need to address the way women are recruited into tech. STEM subjects (Science, Technology, Engineering and Maths) are not the only areas Data Science candidates can come from, but this is where they are most widely recruited from. We need to change and update this conversation. For example, having a marketing degree can set up you up for a great career within data marketing and insights if given the correct training on programming. 

What do you love most about working in tech? 


We live in an ever-changing world and tech is undeniably at the heart of everything that we do – from Apple Pay to Track and Trace – it’s part of every process. It’s amazing to be a facilitator of this. 

I also love speaking to clients and hearing about their pain points and, with my knowledge and expertise, being able to offer tangible solutions. 

Does being a woman give you any advantages in the tech sphere?   


I think those inherently female skills, such as empathy and the ability to listen and understand on an emotional level, have certainly helped me in tech. I can make those around me feel like we’re working together, not against one another - which is very common between two males within the industry. 

Who is your biggest role model for women in tech and why?  


Sunmee Jang – she works for Sony Playstation as its Global Analytics Manager. She is one of the best people I’ve ever come across in tech, woman or man! She is incredibly solutions-focused and intelligent, running a team that produces some of the best games in the world.   

Not only does she hold impressive skills, her kindness is second-to-none. She is empathetic, lighthearted and just an all-round lovely human being. 

For the next generation of women in tech, what advice would you give?  


Think about what you actually want from a career and how the company you're interviewing for fits with that. Look at what the deal breakers are for you and don’t be afraid to ask companies if they offer those things. 

Men are traditionally more likely to demand and negotiate, whereas women aren’t.  However, I would encourage you to do so. It’s not unprofessional to know what you’re looking for in a role. In fact, I would argue it’s an attractive quality in any candidate as you will come across as driven. 

Also, don’t be afraid to shout about your technological achievements! Make a log of these for potential employers as examples when interviewing to open more doors for yourself. So even if you have a gap in your career, your achievements make up for it - don't give anyone an excuse to deny you from that role or promotion. 

Related blog & news

With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

Visit our Blogs & News portal or check out the related posts below.

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From Broken Data Pipelines to Broken Data Headlines

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The data pipeline for Test & Trace will look something like this:    a patient manually fills out a web-form, which automatically updates a patient listfor each test, the laboratory adds the test result for that patientthe lab sends an Excel file to Public Health England with the ID’s of positive patientsPHE manually transpose the data in the Excel file to the NHS Test & Trace systemthe NHS T&T system pushes each positive patient contact details to NHS T&T agentsfor each positive patient, an NHS T&T contact centre agent phones them. This is a not a single pipeline because in the middle a human being needs to open up an editable file and transpose it into another file. The pipeline is therefore broken, splitting at the point at which the second Excel file is manually created. If you put yourself in the shoes of the person receiving one of these Excel files, you can probably identify several ways in which this manual manipulation of data could lead to harm. And it is not just the data which needs to be moved manually from one side of the broken pipeline to the other side, it is the associated data types, and CSV files can easily lose data type information. This matters. You may have experienced importing or exporting data with an application which changes 06/10/20 to 10/06/20. Patient identifiers should be of data type text, even if they consist only of numbers, for future-proofing. Real numbers represented in exponential format should, obviously, be of a numeric data type. And so on. One final point: the different versions of Excel (between the Pillar 2 laboratories and PHE) are a side-show, because otherwise this implies that had the versions been the same, then everything would be fine. This is wrong. The BBC have today reported that “To handle the problem, PHE is now breaking down the test result data into smaller batches to create a larger number of Excel templates. That should ensure none hit their cap.” This solves the specific Excel incompatibility problem (assuming the process of creating small batches is error-free) but has no bearing on the more fundamental problem of the broken data pipeline, which will stay until the manual Excel manipulation is replaced by a normal and not particularly complex automated process. Curiosity So where does curiosity fit in? The first thing that any Data Analyst does when they receive data is to look at it. This is partly a technical activity, but it is also a question of judgement and it requires an element of curiosity. Does this data look right? What is the range between the earliest and the latest dates? If I graph one measurement over time (in this case positive tests over time), does the line look right? If I graph two variables (such as Day Of Week versus positive tests) what does the scatter chart look like? Better still, if I apply regression analysis to the scatter chart what is the relationship between the two variables and within what bounds of confidence? How does that relate to the forecast? Why? This is not about skills. If I receive raw data in csv format I would open it in a python environment or an SQL database. But anyone given the freedom to use their curiosity can open a csv file in Notepad and see there are actually one million rows of data and not 65,000. Anyone given the freedom to use their curiosity can graph data in Excel to see whether it has strange blips. Anyone given the freedom to use their curiosity can drill down into anomalies. Had those receiving the data from the Pillar 2 laboratories been allowed to focus some of their curiosity at what they were receiving they would have spotted pretty quickly that the 16,000 patient results were missing. As it was, I suspect they were not given that freedom: I suspect they were told to transpose as much data as they could as quickly as possible, for what could possibly go wrong? Single Data Pipeline, Singular Curiosity: Pick At Least One To reiterate, the current problems with T&T would never have arisen with a single data pipeline which excluded any manual manipulation in Excel. But knowing that the data pipeline was broken and manual manipulation was by design part of the solution, the only way to minimise the risk was to encourage people engaged in that manual process to engage their curiosity about the efficacy of the data they were manipulating. In their prototype phases – for that is the status of the T&T application - data projects will sometimes go wrong. But they are much more likely to go wrong if the people involved, at all levels, do not have enough time or freedom to think, to engage their curiosity, and to ask themselves “is this definitely right?” You can view Moray's original article here.  Moray Barclay is an Experienced Data Analyst working in hands-on coding, Big Data analytics, cloud computing and consulting.

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