Becoming a Data Scientist

Data science is a vital component of decision-making in today’s data-driven world.

Data scientists have the critical role of interpreting data, highlighting insights and leveraging this to add intelligence in decision-making. 

To provide insights into the journey of becoming a data scientist, we sat down with Dan, a Lead Data Scientist at Liberty IT. Dan has worked across various sectors, including cancer research, drug design, mobility, and insurance. 

Why Become a Data Scientist

Let's start with your journey into data science. What led you to pursue a career in this field?

My journey into data science was somewhat serendipitous. Initially, my love for mathematics and physics steered me towards a career in STEM. However, it was during my university research project that I discovered the fascinating intersection of biology and technology. 

This experience sparked my interest in computational biology, ultimately leading me to pursue a master's degree and later a PhD in biophysics and drug design. 

The tangible impact of data science on solving real-world problems, such as screening for cancer and developing new drugs, solidified my decision to pursue this career path.

It's interesting how your background in biophysics shaped your trajectory into data science. Can you tell us about your transition into your current role at Liberty IT?

After completing my PhD, I faced the common dilemma of whether to continue in academia or transition to industry. I chose the latter, and my journey led me to work on cutting-edge technologies in the mobility sector and then transition to Liberty IT. 

It was the opportunity to tackle varied challenges, from computer vision to predictive modelling, and more recently GenerativeAI, with Liberty IT's emphasis on innovation and learning, that drew me to this role.

Essential Skills for Aspiring Data Scientists

Your journey highlights the versatility of data science and its applications across industries. What do you consider are the essential skills for aspiring data scientists?

Becoming a successful data scientist requires combining technical expertise and soft skills. On the technical side, proficiency in programming languages like Python, R and SQL, along with a strong foundation in mathematics and statistics, is crucial. 

However, equally important are soft skills such as communication, critical thinking, collaboration, creativity and attention to detail. Data scientists must be able to translate complex insights into actionable recommendations and work effectively in interdisciplinary teams. 

Bridging Data Insights with Decision-Making

That's a valuable insight. What obstacles have you encountered in your career, and how have you overcome them?

One of the biggest challenges in data science is effectively communicating insights to non-technical stakeholders. It's tempting to rely on jargon and buzzwords, but honesty and transparency are paramount. 

Admitting when a problem may not have a data-driven solution and having difficult conversations with stakeholders requires integrity and courage. Overcoming these challenges involves fostering a culture of open communication and advocating for ethical and unbiased practices in data science.

Advice for Aspiring Data Scientists

Your emphasis on honesty and transparency resonates with the ethical considerations in data science. What advice would you offer to individuals considering a career in data science?

My advice to aspiring data scientists is to embrace curiosity and focus on developing a habit for continuous learning. Data science is a dynamic field that requires adaptability and a growth mindset.

Don't be afraid to ask questions, seek mentorship, and challenge conventional wisdom. Imposter syndrome is common, but remember that growth occurs outside of your comfort zone. Stay curious, stay humble, and never stop learning.

How to Become a Data Scientist? 

For those interested in pursuing a career in data science, what steps would you recommend they take to get started?

The journey to becoming a data scientist begins with acquiring the necessary skills and knowledge. Start by gaining proficiency in programming languages like Python, R, and SQL and mastering fundamental concepts in mathematics and statistics. 

Look out for free courses too. Depending on your own passion, learn the commonly applied techniques and current domains, from harnessing the power of generative, open-source models to developing a simple, story rich visualisation. 

Additionally, seek opportunities to work on real-world projects, either through internships, work experience programmes, online courses, or personal projects.

Networking with professionals in the field and seeking mentorship can also provide valuable guidance and insights. Remember that becoming a data scientist is a continuous journey of learning and growth. 

Is Being a Data Scientist Hard?

Many people perceive data science as a challenging field. From your experience, how would you describe the difficulty level of becoming a data scientist?

Data science and its career paths certainly have their challenges, but I believe the level of difficulty varies depending on individual backgrounds and experiences. 

For those with a strong foundation in mathematics, statistics, and programming, the technical aspects may come more naturally. However, mastering the soft skills, such as effective communication and collaboration, can be equally challenging. 

Like any profession, becoming proficient in data science requires dedication, continuous learning, and a willingness to embrace challenges.

Start your journey into Data Science at Liberty IT

Want to learn more about what a career in data science looks like at Liberty IT? Naomi Hanlon, our Principal Data Scientist, talks about some of the projects she’s involved in and shares advice for anyone wanting to work in data science. 

For those inspired by Dan’s journey and interested in a career in data science, visit our career page to explore current roles.