Transition into Tech: The Power of Transferable Skills

Breaking into the tech industry with a non-technical background can seem extremely daunting, especially as a new graduate. Leaving college with degrees in Neuroscience and Environmental Studies, I knew that I had to shape my skills to fit an unfamiliar industry. Although I knew I wanted to make the jump into tech and data, I didn’t know just how valuable my non-tech background could be in this sector.

Working in tech and data requires diversity of thought. I’ve found it beneficial to enter the industry without a traditional computer or data science background because it allows me approach business problems with a different lens; I know what it is like to be on the other side of a technical tool without an understanding of how it works. Since I can put myself in the shoes of a non-technical business user, it is easier to scope and develop simpler, more cost-effective solutions. Instead of trying to develop the most complex, high-tech tool that may not translate to a business audience, wasting time and resource on a product which does not get adopted, I can create something that is tangible for a non-technical audience to use.

Moreover, data exists in and is transforming every industry. Having subject matter expertise is important to contextualise data within its sector. These nuances may be arbitrary to others that are unfamiliar with the environment that the data exists within. For example, data is becoming an increasing vital resource for life sciences and pharmaceuticals, helping to better understand patients’ needs and so improve the safety and efficiency of developing and trialling treatments. Combining domain knowledge and experience from biomedical backgrounds with data skills can help data teams to better use patient data, with understanding of the dangers of biases and sensitivity of the data they are handling.

Analytical thinking is another important skill that I’ve recognized when working with data. Problem-solving skills taught in other disciplines can be applied to technical problems. Those coming from STEM backgrounds like me may be specifically familiar with taking concrete knowledge and applying it to a critical thinking question. No one dataset is created equally, meaning that you need to be agile when proposing solutions to data-related problems that not only solve the issue but also comply with stakeholder requirements, budget constraints, and the available technologies.

Communication and presentation skills are also valuable. Being able to translate technical information to a possibly non-technical audience, i.e., senior stakeholders, is vital for supporting the evolution of businesses. You need to be able to demonstrate how a technical solution positively impacts the business at a larger scale and why it would be worthwhile to implement that solution. Being able to show the overall picture, as opposed to zeroing in on small technical details, will resonate better with your audience and help breakdown the siloes between the business and technical teams which often hold organizations back from embracing data and technology.

Ultimately, a willingness and hunger to learn is critical. Tech and data are constantly changing, whether that be through the release of new software, coding languages, and even shifting industry standards. Take the recent global phenomenon of ChatGPT, an AI chatbot which is disrupting search engines and content creation. Within weeks of its launch, this tool had the world questioning if it could replace the industry-leading search engine Google, as well as raising concerns about bias, its use in education and software development, and more. For those in the data and AI world, ChatGPT has prompted countless conversations about its ongoing evolution, with a focus on driving continual learning in order to advance and evolve this technology. Like other research-based industries such as medicine, it is important to stay up to date with current developments to stay relevant.

Although highly technical, data still requires creative solutions. Data visualisation is a form of storytelling, transforming data into information which easily digestible, contextualized, and even interactive to capture the attention and understanding of business users with insights that will improve their decision-making. Having a strong visual component as a final deliverable will help to communicate your work to other stakeholders. The better this tool looks and functions, the more receptive people will be to outcomes of the project. Creative approaches to business problems are often underrated but are key in overcoming barriers like limited resources or budget. Sometimes, you need to be able to think outside of the box to provide a solution which addresses the challenge at its core and makes the most of whatever you have at your disposal.

Sydnie Lesser is a Data Product consultant working to support organizations in the US to evolve and embrace next-generation technology with impactful solutions which drive change. To learn more about how Kubrick helps businesses access the talent and skills needed to meet their transformation goals, get in touch: speaktous@kubrickgroup.com.

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