Women in data: Interview with a Data Scientist at Simmons Wavelength

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

I had the pleasure of hearing from Daisy Mak, a data scientist at Simmons Wavelength who explained how one of the biggest challenges as a data scientist is the lack of understanding around the role; it’s much more than just handling data, it’s about exploring the data and getting insight out of it.

This is a brilliant interview that showcases first-hand what it’s like to be a data scientist, offering valuable insight to other females or males wanting more clarity over the role, as well as businesses who are considering expanding their data team.

What interested you to start a career in data science?

For people who studied physical science and have research experiences, data science is a natural choice as alternatives to academia jobs. The elements of applying the latest technologies to businesses and problem solving are very similar, and that is why I feel very at home in having a career in data science.

How has Covid-19 affected your role as a data scientist?

Very little to be honest. For most data scientists who can obtain data online or send to the client, we can almost do all of our job anywhere as long as we have the computer and internet. The exception would be tasks that involve very sensitive data that one has to work onsite at client’s office or where you need a secure device to access the data. There is only one project that I have worked so far that falls into this scenario but we managed to solve it by asking the client to send their secured laptop so I could access the data.

What are the biggest challenges facing data scientists right now in your industry?

There are two main challenges:

  1. Still many companies don’t understand the role of a data scientist. Sometimes they think they need a data scientist simply because they have some data needs to be handled, but in fact the tasks can be handled by analysts or engineers. It is true that data scientists are capable to tackle anything about data, but it should be more than that: data science is about exploring the data and getting insight out of it. It is the latter part that distinguishes data scientists and data analysts/engineers.
  2. The technology is advancing fast. For example, the use of transformers (e.g. BERT) has changed the entire NLP field. Most of the machine learning tasks are now about BERT. So, one should be constantly reaching out the latest research to keep up with the tech. But in reality, most of us are so busy with constant client deliverables and deadlines, that it is hard to keep up. Some companies are very supportive of constant learning by having an innovation day, in which they allow their staff to spend a few days on non-business activities in order to learn new things.
Just 15% of data scientists are female with research studies stating that perceptions among young females are that it’s not an attractive career option. Do you agree with this statement? And if yes, what more do you think the industry can be doing to attract and retain female talent?

For me, gender imbalance is not a factor when I considered to pursue data science as a career. I believe the tech industry now is doing a good job in promoting gender balance and sometimes I feel it is an advantage being female!

Having said that, I think family planning is a big issue for females to develop their career. I have female colleagues who have employment gaps simply because of childcare. It is hard to avoid this when society still see women as having to sacrifice their career when it comes to family planning.

But I think most companies are improving this by giving equal parental leave allowance to men and women, so that new mothers can return to their work. This really helps a lot and is one of the things that the industry can do to help to retain female talent.

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

For me, NLP is a very exciting field as there are many breakthroughs during the past few years. The theoretical research about question-answering, decision making, etc. are becoming more practical that they finally can apply to business use cases. Industries such as medical and legal would be beneficial from such advances. The state of the art models like Transformers are definitely things to look out for.

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