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What it takes to be a data scientist

What it takes to be a data scientist

How do you inspire more people to enter this important and growing field that is having a major impact on every sector of our society?

Professor Rosalind Archer, Katarina Kolich (BNZ), Lisa Chen (Harmonic Analytics), Amanda Hughes (Nicholson Consulting), Sarah Cawsey and Agate Ponder-Sutton (BNZ), and Sarah Quintal (LPS)

Professor Rosalind Archer, Katarina Kolich (BNZ), Lisa Chen (Harmonic Analytics), Amanda Hughes (Nicholson Consulting), Sarah Cawsey and Agate Ponder-Sutton (BNZ), and Sarah Quintal (LPS)

Working in data science is fun...And while it is deeply technical, it is also creative.

Agate Ponder-Sutton, Bank of New Zealand.

Katarina Kolich, head of enterprise data services at Bank of New Zealand, continues her advocacy to encourage women to consider a career in information technology.

Next month, on March 5, she is leading the New Zealand event of the annual Stanford University’s Women in Data Science.

The one-day technical conference will feature world-class female data scientists in industry and academia across a wide range of domains. It will be held at Stanford University, and at 150-plus regional events in more than 50 countries, will be broadcast via livestream and Facebook Live..

Women data scientists will talk about the importance of the role and help inspire and educate data scientists worldwide, regardless of gender, says Kolich, who is New Zealand ambassador to the annual event.

The New Zealand speakers include Government Statistician and Stats NZ chief executive Liz MacPherson, Professor Rosalind Archer of the University of Auckland, Dexibit CEO Angie Judge, Bank of New Zealand data scientist Agate Ponder-Sutton, PwC analytics manager Pieta Brown; Harmonic chief analytics officer Dr Lisa Chen, senior data scientist at Nicholson Consulting Amanda Hughes, Data Futures Partnership chair Dame Diane Robertson and actuarial analyst Emma Vitz.

Liz MacPherson
Liz MacPherson

WiDS founder Professor Margot Gerritsen at Stanford University has a New Zealand connection.

“I began my academic career teaching at the University of Auckland, prior to returning to Stanford in 2001," says Gerritsen, who received her PhD in scientific computing and computational mathematics at Stanford University, where she is now director of the Institute for Computational and Mathematical Engineering.

Katarina Kolich with Professor Margot Gerritsen director of Stanford ICME and founder of Stanford WiDS in October 2017. Gerrittsen taught at the University of Auckland before moving to Stanford University, where she had completed her PhD.
Katarina Kolich with Professor Margot Gerritsen director of Stanford ICME and founder of Stanford WiDS in October 2017. Gerrittsen taught at the University of Auckland before moving to Stanford University, where she had completed her PhD.

We aim to inspire more women to enter this important and growing field that is having a major impact on every sector of our society

Professor Margot Gerritsen, Stanford University

"The speaker line-up is fantastic, featuring female New Zealander data scientists from academia, government, and industry, focused on multiple domains," she says of the Auckland conference.

“By showcasing the amazing work that these incredible women are doing, we also hope to reinvigorate women currently in fields related to data science."

“We aim to inspire more women to enter this important and growing field that is having a major impact on every sector of our society.”

When asked why it is important for women to be in data science, Gerritsen, in an interview about how she started WiDS, replies: “More and more decisions in the world are being driven by data.”

Dame Diane Robertson
Dame Diane Robertson

“Management decisions, political decisions, industrial decisions, commercial decisions and many others… are made based on data analysis.

“When you are finding patterns in the data, when you do machine learning and try to make predictions in the future based on the data, you are really influential.”

Angie Judge, from software engineer to Dexibit CEO.
Angie Judge, from software engineer to Dexibit CEO.

But she says most data science teams are composed of men who are white or Asian.

“You may say these scientists look at the data very analytically, bias doesn't come in,” she said during the interview. “That is wrong.”

“Data  is very  malleable. You can see in data often what you are looking for. In other words, bias can creep in really, really quickly.”

“It is clear that more diverse teams ask different questions. They probe data in a different way and they come to different conclusions.”

She adds having more women in these teams is a logical thing, so why would a company not tap into that talent pool?

The speakers and panelists at the upcoming WiDS conference in New Zealand reiterate Gerritsen’s message.

Some of them spoke to CIO New Zealand, at a gathering organised by  Professor Rosalind Archer and Katarina Kolich at the University of Auckland.

“Working in data is fun,” says Agate Ponder-Sutton, a data scientist at the Bank of New Zealand. “Data science is about telling the stories found in data.

“You get to find the puzzle and solve the puzzle and build the story,” she adds.

“You can tell a certain kind of truth within a data set that is not something you can see just by looking. You have to use skills you have to build it. And while it is deeply technical, it is also creative.”

Ponder-Sutton points out, however, that women are underrepresented in Science, Engineering, and Technology (SET).

According to the US National Center for Women and Information Technology, the number of women in computing have steadily declined since 1991 and 30 per cent of women in SET felt isolated or stalled. 

The NCWIT survey also found that women are twice as likely to quit their job than men in the high tech industry (41 per cent to 17 per cent).

“This feeling can be linked to a lack of mentorship and support,” she says. 

I always feel women can do well in this field

Dr Lisa Chen, Harmonic

The same report cited a Center for Talent Innovation study, which found nearly half of the women who left the SET private sector continued to use their technical training in jobs in other sectors like not for profit, government or startups. The remaining 31 per cent stayed in the workforce, but took a non-SET job.

“The WiDS conference and those like it are about bringing awareness to the current situation, creating the support for and recognising for what women bring to the field of data science,” says Ponder-Sutton.

Professor Rosalind Archer says what is also important is what the data scientist can bring to the organisation, to society.

“You can make real impact on the world with this kind of stuff, whether that is social, corporate, or government,” she says. “The idea [that] you can make a real impact does keep people motivated in technically demanding fields.”

The challenge of getting more women to go into the field of data science is a key theme in the conversation.

Lisa Chen, who studied computer science and has a doctorate in statistics at the University of Auckland, recalls that in class, she often found herself to be the only female.

You do not come up with adequate strategy without adequate data.

Sarah Cawsey, Bank of New Zealand

She says she did quite well in her technical classes. “That is a sign that women can do it even if it is really a technical and difficult subject.”

“I was always working in more male-oriented sectors,” she says.

“I always feel women can do well in this field,” she says. “I always think we have an advantage. I have a lot of empathy, I talk to clients and understand their requirements.”

Archer, meanwhile, notes how the University of Auckland has made a real standing commitment for diversity.

“We had someone employed full time on our staff since 1989 to recruit and retain women students. That is real commitment,” she explains.

Amanda Hughes, senior data scientist at Nicholson Consulting, underscores the importance of working to ensure the diversity of data team members.

Hughes has worked as an analyst at Pharmac and Stats NZ. Her first job was at the latter, where she found a good split of males and females in the teams.

The census team members were predominantly female, while those in the business and economics teams were mostly males.

“That was interesting,” she thought then, and left for her overseas experience. On her return, she was placed on the economics team.

“Within six months, we had turned it into a 50-50 male and female team,” she says.

Sarah Quintal, client director at LPS, says a positive development in the industry is more women are leading big change programmes and involved in IT services delivery.

The landscape is really changing, says Quintal, who started working in the ICT recruitment space.

“Across IT definitely in the delivery space you see far more females,” she adds, but still sees lack of diversity in more technical roles like in engineering and architecture.

“People are really adopting agile as a methodology, and the culture of agile really lends itself to females. We know how we would approach things in a pragmatic way.”

Quintal says working in data is a good career move.

“There is a shortage of people working in data science and there is a strong demand. People will be snapped up with their skills and they will be embraced and looked after.”

Sarah Cawsey, head of enterprise insight at Bank of New Zealand, says her current role has been due to the business experience she has built up over time.

Her work has mainly been in strategy. “You do not come up with adequate strategy without adequate data. And that has always been the way I approached it, bringing the right data in order to create the right stories.”

“We were able to get into spaces we thought we were never able to get into before. [This was]  because we are able to explain what the data scientists and engineers are able to do, and what difference that can make to the business.”

Kolich says her colleagues in data science who are presenting at the conference represent a wealth of experience.

“Can you imagine if you were a 17-year-old just starting at university, not really sure if you are doing the right thing by signing up for that coding class, or advanced statistics? And then you see Amanda, Lisa and Rosalind [talking about what they do], you will say, I think I can see where I am going to get to.”

Katarina Kolich at the Stanford Institute for Computational Mathematical Engineering
Katarina Kolich at the Stanford Institute for Computational Mathematical Engineering


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Tags big dataanalyticsdata scientistSTEMwomen in technologyStanford UnivesityKatarina Kolilchwomen's month

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