Women in Data Science

Featuring Ulrike Wood-Sichra, Data scientist and Senior Research Analyst (IFPRI)

Ulrike Wood-Sichra

Ulrike Wood-Sichra

Data scientist and Senior Research Analyst (IFPRI)

I have various roles in my working group. The main one is looking after the Spatial Production Allocation Model, or SPAM – nice name, huh? This entails assembling, at a global level, the data necessary to run the model, and present the results to the wider public in a variety of formats including maps.

The second place is taken by the Dynamic Research Evaluation for Management (DREAM) package. Here also I collect and feed data into the model, and then produce results for the scenarios requested by the client. The rest of the time – is there any left? – is taken by smaller requests from my colleagues related to SPAM output, general agricultural statistics and other data which they might need for smaller projects, proposals for future activities, background material for a journal, etc.

What led you to pursue a career in data science?

My first love was mathematics and computer science. My first job was at an international organization (IIASA) dealing with models, mainly related to food and agriculture. I have always enjoyed putting structure and order into things, in this case, numbers.

As my experience and exposure grew, so grew the amounts of data I would handle, the number of sources to consult and the need to catalogue and code all the identifiers by which we can distinguish one datum from another. I did not specialize in any field by have always tried to benefit from the knowledge out there and have searched for ways to include this in my data work.

What are the things you love most about your role as a data scientist?

It’s the magic that happens when properly structured and coded data is easy to find and you can assemble complex tables with just a few steps – when you know where the data is and how to find it.

Closely related to this is the ease with which one can move from one data format to another, thanks to a plethora of conversion routines or just plain simple programming.

What has been the most exciting project you have worked on, or the one of which you are the most proud?

I don’t know if you can call SPAM (Spatial Production Allocation Model) exciting. But when I realise that every day there are new users downloading SPAM results from our website makes, I can see that we have created a worthwhile product. Not just in academia, but also in the private sector.

Do you feel this is an exciting time to be a woman working in the data sector?

I have always felt that it is exciting to work with data, X years ago – I am not disclosing my age, although it is easy to deduce –  when I started, and now. In this case, I don’t make distinctions related to gender.

Why do you think there is a lack of women in tech and data sectors?

Traditionally women were not encouraged to pursue technical careers, and data is very much related to technical activities. Women were perceived as not logical thinkers! The technical/data sector was not emotional!? When I started at university there were only 4 women among 80 students!

But this ratio is changing, at least in the developed economies. The lack of women in technical sectors can also often be attributed to cultural biases which, fortunately, are also crumbling under the weight of globalization and broader availability of information.

As a woman, did you face any obstacles or challenges in your career pathway to become a data scientist? Any advantages due to your gender?

No, I did not face any obstacles in my career related to my gender – or at least I did not notice them. But neither did I see advantages being a woman. The fact that I never had children to look after, and that my partner was always understanding of my needs to travel or put in long hours, have probably contributed to my relative freedom.

Why should more women get into data science?

As I see it, data science is very much related to accuracy, attention to detail and dedication. And these are characteristics I often find in women around me. They would be perfect handling data – even though it is, unfortunately, a bit dry!

How could more women working in data, specifically for the development/agriculture sector benefit the sector?

If more women would work with data in agriculture and development related fields, more gender-specific information would be gathered and be visible. In my experience, it always takes extra effort to elicit information related to women. If more women were involved in the data collection, methodology, and processing, our attention would automatically – or so I hope – focus more on women’s issues and contributions in development and agriculture, and more gender-specific data would become the norm.

What advice would you give to women wanting to get into data science?

Keep up with the trends, learn new technologies and methodologies. Make GIS part of your toolkit. Interpretation of remotely sensed data is increasingly important. Experiment with big data – and get your workplace to allocate a terabyte of data for you to play with it!