Women in Data Science

Featuring Alma Carolina Rivera, Data Coordinator (CIMMYT)

Carolina Rivera

Carolina Rivera

Data Coordinator (CIMMYT)

I am Data Coordinator within the IWYP project. I am based in CIMMYT Mexico and within my role, I am in charge of two main activities:

  1. To enhance data capture and curation workflows: Develop and implement new technologies for data capture and develop efficient systems and protocols for standardizing, collecting, compiling and curating field data generated in the IWYP Hub, with a special focus on data quality control.
  2. Support data storage, sharing, and publishing Provide better support for loading field data generated in the IWYP Hub (Obregon) and by IWYP Partner projects on institutional databases and repositories (such as Germinate and Dataverse), and facilitate data access and sharing by GWP and IWYP scientists and staff, as well as the broader research community.

How did you come to pursue a career in data science?

I am relatively new to data science and the IWYP Data Coordinator is my first role focusing entirely on data workflows. I have almost a decade working in Wheat Phenotyping, where I have been a witness to the fast progress in protocols and technology used for data collection, processing, and sharing. During my Ph.D., I collected a big amount of phenotypic data and realized all the diverse sources of error in the most common phenotyping and agronomical characterization protocols. Knowing the biology behind, it is very interesting and fulfilling to help to improve the quality of the data we take and that is one motivation I had to join this project. Moreover, I realized how important in terms of impact is to share data within the scientific community, and in this respect having accessible databases is crucial.

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

I love that I can interact with scientists from different disciplines, I am happy to know I can help scientific results to become more spread across scientific communities and even publicity shared. I love that even though in my job I am focused on data quality, management and sharing I can still work in the field and interact with the data collection process.

What has been the most exciting project you have worked on?

Although as I said above, I new to data science I have had very good experiences working with an amazing team of data scientists. The biggest achievement in my role has been the release of the public version of the Germinate database for IWYP, along with colleagues from the data management team, the IT Unit and all the data shared by the IWYP and other wheat scientists

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

Yes, I think the participation of woman in the Data/Tech sector is increasing and more opportunities are opening for us.

Why do you think there is a lack of women in Tech/Data sectors?

Generally speaking, I think very often there is not a clear career path for data scientists, especially in the agricultural sector. Most people I know focusing on data in agriculture have a different range of diversity in major degrees, even in PhDs, and these are not particularly related to data science. So, I guess it takes time for us to gain a stable position as a data scientist. Regarding women in this sector, first of all, I think there are fewer women studying majors in data or computing science. Secondly, I have read that women often feel less confident than men even if they have the same education and experience levels. Those issues have to be addressed. In addition, there is the often a lack of flexibility when seeking a balance of career and family.

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

Personally, one of the main challenges I faced is not having a considerable amount of experience in the field of data science but that is never an obstacle. I am grateful I have had good guidance and that I am involved in such a great project – my main mentor is a woman! I do not think I have any advantages because of my gender, and I would like to think I have no disadvantages either, but we would have to check current studies on the subject.

Why should more women get into data science?

Because data science is essential nowadays, big data processing and management are driving very important decisions on a daily basis and women have a lot to offer. We should not allow our gender influence employers and our own decisions. It is time to forget all the misconceptions around what a woman can or cannot do. Women should be involved in every discipline of science and in an equal way to men.

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

Data science is a rewarding, fast evolving and fun career that should be open equally to both genders. Do not allow people, cultural ideas or misconceptions affect your career decisions. Be aware of challenges you might have and if you ever feel gender or stereotype bias by employers or colleagues, try to raise the issue.