Insights

The Data Series: Interview with a Data Scientist at Carnall Farrar

Article written by Jacob Knight, Head of Data of X4 Technology.

In this modern and complicated time of economy, translating data into actionable insights is an essential process to any large organisation. Decisions based on statistics and trends empowers businesses in any industry to makes strategic choices on ways to improve and target audiences.

In healthcare, the use of data and the role of a data scientist means that even the most complex of problems can be solved and data science is even helping to open up new fields in terms of research and experiments.

I spoke to Chris Monit, a Data Scientist at Carnall Farrar to hear about his journey into data coming from a computational biology academic background and he also shared his excitement on future developments in medical and biological data science.

What interested you to start a career in data science?

I had worked in computational biology academic research previously, which is effectively data science applied to life sciences. I was keen to take what I’d gained from academic science and apply it to more tangible problems.

What do you enjoy most about being a data scientist?

It’s about being creative for me. I enjoy building software, especially tools that will be used by other people, and designing models for studying interesting problems. I also really enjoy learning new skills and tricks, which is a regular theme of this work because often you’re confronted with a problem and you need new tools to tackle it.

What real challenges do you face as a data scientist?

There is a constant tension between developing thorough techniques and quick solutions. In academic research you take your time to develop the most suitable model for your problem but clients don’t care how you solve a problem, they want an answer quickly. So you have to develop strategies for finding approximate answers fast and iterating as time allows.

What excites you most about recent developments and the future of data science?

In medical or biological data science I think we are just at the start of what could be achieved. Now people are training machine learning models to imitate human experts using the same input data humans use, like x-ray images. But the advantage of machine learning is allowing the system to discover its own features in the data to exploit and there must be entirely new measurements that will provide predictive signal, measurements which might seem indirect or unintuitive.

Any words of wisdom to other aspiring data scientists just starting out?

Because the area is still new, people coming into it tend to have had unique experiences and expertise, which is a great thing for a creative industry where diversity of thought is valuable. So use your own interests and experiences – as they say to aspiring authors, ‘write what you know’. Look into problems and tools you find interesting and reach out to people interested in the same things.

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