Samantha Rhynas

Head of Data at Effini & Girl Geek Scotland Leadership Team & PyData Edinburgh Organiser

Tell us about your journey to your current role now.

So, I’m a software engineer and specialise in data science.

I left school at 17 and didn’t go straight into university. I didn’t know what I wanted to do. In hindsight, that’s so normal but at the time it’s really hard and you don’t realise that it’s very human. I got a job as a research scientist and was there for 7 years. I loved that experience, the advantage of working with really smart people is that you can learn from really smart people. It’s a lesson I took with me and something I now say to people, you know, ‘use’ the knowledge of the people in your workplace.

My Dad was also a real gadget man, he had been an engineer his whole life.  For his generation, most didn’t go in to higher education, especially those who lived in places like he did – he is from the Highlands.

I’ve always been very creative and my Mum and my Dad both stimulated that in me – maybe my Mum in a more traditional feminine way, knitting and so on. A lot of female engineers seem to knit and I have a few different theories about why that is…

A couple of years later, the company where I worked was bought and they wanted to shut down the Scottish office. I was only 24 at the time and not sure what to do – I’d started a life sciences degree as a day courses at college but didn’t feel it was the right thing to continue with. I was given a small lump sum for redundancy – which felt like millions at the time – and I thought that could support me through university.

I always say to people: you don’t need to take a traditional route. You don’t need to have all the training out there to be a software engineer. You don’t need to be coding since 5 years old – I really disagree and it’s such a hindrance to not just women but to people who have had very different backgrounds and opportunities. I had 5 Highers and I thought ‘am I clever enough to go to university?’ At 24, I had a house and long-term partner, I was in a different place to many people my age. However, I went for it and got unconditional offers from all 5 that I applied for.

I went to Heriot-Watt and did computer sciences. I didn’t really enjoy first and second year but loved third and fourth because I figured out how my brain worked. It also gave me a window in to the gender issues surrounding software engineering. When I began, there were about 10 women in about 140 students. By fourth year, there were 60 of us left and 2 were women. The other woman told me a very interesting story – she had done a Higher National Certificate in computing before and it was pretty much all women. She worked briefly in the industry and then left to have kids. When she came back to it in the university environment, she found there were pretty much no women. That shift happened because computing used to be seen as very much a woman’s role, as it was administrative.

I now am involved with the volunteer group Girl Geek Scotland, running workshops and networking events to help build a community & support women in Tech. This work also provides role models and advice that university often omits and that looking back, I didn’t have.

I knew one thing when I finished university – and I regularly have conversations with people about this – that I didn’t want to go in to a big company, I felt I’d have more opportunity in a smaller one.

I know many people who have done tremendously well and risen through the ranks in larger businesses, whether starting in junior roles or specific apprenticeships and I think this is something we should think about with regards to Data science & software engineering in general.  There is such an incredible community in Scotland, but it needs to do better in terms of diversifying its recruitment streams.

I’ve found Data science in particular very focussed on academic prowess, but there are so many people without PhDs doing amazing jobs in the industry, we’re too obsessed with academic qualifications, but let’s look at people as a whole, and what their potential is and bring many different people into the sector.


There is a skills gap there and I hope the City Deal really thinks about how they’re going to engage people in Edinburgh across all areas, including those who don’t (yet) know what data & data science is all about – whether that’s school leavers, those working in totally different roles, returners, and anyone who wants to re-train. This is central to inclusive growth and for providing opportunities for everyone in the area. There is money being pumped into the city – we need to make it benefit all.

.. If we do it right, we could become a template for other countries, other cities!


Diversity & inclusivity are very broad areas – let’s not wait and then blame “the pipeline’ – let’s try and make change right now

Businesses get so excited about saying they’re doing machine learning, AI or data science, and often recruit heavily from the academic side to immediately have folk with specialist knowledge, but other critical skills come from other areas. I fit very well in to the data science sector because I don’t come from this background. I may have areas that I’m not familiar with, and that’s OK. My  hands on business and software real world experience brings qualities that allows companies to build meaningful solutions.


So what did you do when you left university?

I applied for a job with Quadstone because I was attracted by the job advert. It used language that really resonated with me, like ‘investigate’ and ‘problem-solving’.

I’d just like to mention the interview because it was very interesting – there were two parts, a technical interview, which I was very worried about because I wasn’t confident about my development skills. That is a big area I hear people getting very stressed about – technical interviews put people off even applying for roles.

I was interviewed by two people and there were some things I just didn’t know the answer to, so I talked through the stuff I did and highlighted the things I didn’t, I ended up having a fantastic conversation with the interviewer about the problem I was being asked to solve, so I learnt many new things and they could see my potential. You don’t need to know everything, it’s a chance to demonstrate how you cope with not knowing things in your job, which is totally normal!

I got the job and I worked there for ten years. I was working on software which was essentially a predictive modelling tool. I was developing automated test frameworks and continuous build systems, writing tests and learning about the tool. We worked with financial institutions, banks, supermarkets and we were working with ‘big data’. This was the start of my experience with what is now called data science.

I got promoted into lead roles and took on more management responsibilities, managing distributed teams.  I started working more with clients, integrating the software we built into their environment and it made me realise that’s skills I have  communicating with people, customer interactions and finding solutions for people’s businesses all fit really well into the tech world. I’ve always worn multiple hats and enjoy work where I use multiple skills. This is one reason I love working for smaller companies.

A lot of people in data science don’t have a background in software engineering and there are distinct benefits to my background – I can build and deliver solutions that are meaningful and make sense to a business. In my role now, I help businesses define data strategies & roadmaps right through to implementing the solutions themselves. My skill set is often lost in data science – it often focuses on the nitty gritty of process and tech stack and forgets the customer interaction element around a businesses’ challenges & goals.

People often think that tech is about sitting at a desk but that’s so far from the truth – although I do sometimes wish I had more time to be alone and stare at my screen (laughs) You have to be able to communicate to colleagues, the broader business team (who’re doing different roles) and the customers, and view perspectives from different angles.

I then went to work with a company called Aridhia Informatics for 5 years, who’re still around and based in Edinburgh. Their main client was the NHS and we partnered with them to create tools in areas to help support business processes like report generation as well as things like visualisation of patient journeys and started to extend into the use of data science & model building to improve patient journeys.


Tell us about more about a typical day for you?

I work for a company who aim to deliver data strategy and data science solutions to clients, and also deliver on building skills in that area.  We go from one end of the process to the other – from working with teams to understand what we really mean by data & datascience, through reviewing and assessing existing tools and processes & governance, to data insights, predictive modelling, data visualisations & dashboards and implementing those solutions so they become a valued, integrated piece of their production system.  We’re very hands-on and do a lot of implementation work for companies ourselves, or we can integrate ourselves into a team to do that implementation alongside those who are doing it, supporting and building their own internal skills.

The breadth of companies I have worked with is really interesting – sports data, travel companies, financial businesses.

There is lots of press about  rushing in to do AI and data science, they’ve become buzz words –  businesses feel like that have to do those things to compete. With our approach of bespoke solutions, we find that this isn’t always the case. Sometimes businesses don’t have the robust foundations or need to start off much more simply, to work towards more complexity.

 Day to day I might be working on site with a client, or writing proposal, or writing talks, answering emails, or hands on with the data, working on specific projects.


What would you recommend to women and girls who’d like to be in your position?

No matter where you are, do something to just get started. People get daunted by just getting started. Find something you’re interested in and think about what you want to do.   That might be finding an online course to learn about data science, or to teach you coding skills – something like Python is a great background for many roles.  It could also be finding a tech meetup and going along, so you can hear from others, learn what it’s all about, and help to see an area that might be the one for you.  Or it might be going into full time education.  There are risks with all these things, but you’ve got to risk it and go with something if you want to make a change.

There are local meet up groups where you don’t need to know anything to attend, and there are lots of women there ready to share advice. Just get started, you don’t need to leave the job you’re in or sign up for a four-year degree. Engage with groups of people in the area you’re interested in, build your connections that way and talk to people in industry to learn specifically how to get in and what you may work in.

I’m part of the leadership team for both Girl Geek Scotland and PyData Edinburgh, both very different, but I hope everyone finds both welcoming, unassuming, and also informative.


Do you have a heroine in data science?

I would say no. I don’t look to others to say ‘they’re at the perfect point in their career, that’s where I want to be’.

For me, it’s the individuals I meet everyday in business who are getting on with it and doing their jobs.

At the moment, we’re considering a Girl Geek event to celebrate people’s journeys to their jobs and we’re thinking just folk like CEOs etc., we’re picking people ‘down a rung’, if you like, who can actually be role models to people and where others can see ‘oh, they took a rather meandering path!’ and people can be inspired by something authentic and see themselves mirrored and be motivated to change their situation by that.

For the vast majority of people, the profiles we see on Linkedin might seem unachievable and it will make people think ‘I can’t do it’. I’m not saying that for more realistic routes, a data science career will happen overnight, but I am saying that if people are inspired by something realistic, they will make small changes to actually make it happen. Really senior role models are great – but let’s focus on the ‘achievable in next year or two’ sorts of folk too.


Thank you for such a creative and moving response! So, what do you look forward to in your work?

I love getting my hands on new data and getting stuck in to new projects. I’ve had a few conversations in relation to the City Deal and I find it very exciting. That’s why I had my little rant at the beginning (laughs) I really hope it includes a broad range of people outside of graduates and software engineering.

Working with clients is something I really enjoy, listening to the challenges and issues they have, and then working with them to solve them.


Finally, what do you do when the working day is over?

I volunteer. All of Girl Geek Scotland are volunteers, including the leadership team. I’m hugely passionate about it, we can make a difference and really get people to take notice of the challenges women face in the industry.

I also co-organise PyData Edinburgh, our focus is Python and other open source tools for Data Science, which involves monthly meet ups with guest speakers and we sell out every month.

I describe myself as a ‘maker’ – I have a theory I can make anything…which has definitely been disputed along the way, such as outcomes from woodwork classes but I thought ‘let’s give it a go!’.



Image of Samantha Rhynas
Picture by Lesley Martin interviewed by Poppy Gerrard-Abbott

You don’t need to be coding since 5 years old – I truly disagree, it’s an assumption that is such a hindrance to not just women but to people who have had a different upbringing

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