How did you get into data science —Tereza Iofciu
We are starting a new format — not just articles, but also stories!
The idea is to let you get inspired from some fellow Data Scientists from the Community and their stories. As today’s International Women’s Day is an important reminder of gender equality and the empowerment of women, we wanted to take a closer look at women in the data science community. So we decided to kick off our new interview series How did you get into data science with the amazing Tereza Iofciu.
Tereza holds a PhD in Information Retrieval and is the Lead Data Scientist at FREE NOW (formerly mytaxi) Hamburg.
She is the co-organizer of PyLadies Hamburg and PyData Hamburg.
Follow her on twitter @terezaif
How did you get into data science?
I would say I was lucky enough to be at the right place at the right time. I studied computer science in Bucharest, Romania, as I was good at math and physics and IT, computer science, seemed to be something future proof. Then I had the opportunity to come to Hannover and work on my final university project and I stayed on to do my Ph.D. at the L3S Research Institute in the field of Information Retrieval. This now falls under the umbrella of AI and data science.
What fascinates you about data science?
In the past 10 years the field of data science has grown a lot in popularity and thankfully there are a lot more discussions now on the topics of ethics and accountability.
How would you explain to your grandmother what you do in your job?
My grandmother has studied economics and worked as an accountant, so she was dealing with numbers in her job. I would tell her I am also dealing with numbers.
What are your favorite buzzwords?
To be honest, I find buzzwords highly annoying, so I have buzzwords that I don’t like… Big Data.
In which areas can data science solve current and future problems?
Data science can solve problems in an area where data is collected. Data collection is happening in most fields so data science techniques are applied. There are also nonprofit organizations with data science for good efforts in domains where there isn’t an immediate financial benefit to collecting data.
Describe briefly your work at mytaxi
At mytaxi I am leading the Hamburg data science team where we are working on topics ranging from batchlike metrics predictions like revenue and customer value to improving user-facing services such as recommending destinations.
International Women’s Day 2019
The UN theme for the International Women’s Day 2019 is “Think equal, build smart, innovate for change”. Although gender equality has long been an issue, women are still heavily underrepresented in many science and technology sectors.
What do you think is the reason?
I was lucky to grow up in a family where I was never told of things I should or not do or study as a girl, unfortunately, I have often heard different stories from other women, who despite the well-intended advice managed to go into science. Even so, we had less than 10% women ratio at my university. In the IT sector in companies, when they are not having diversity efforts, the ratio seems to be even worse. In my opinion, the imbalance has a lot to do with the stereotypes we currently growing up with and it will take several generations for these to change unless we actively challenge them.
The other problem is that many jobs still have a long list of requirements, usually all together unrealistic to find in one candidate. People who think that all skills are indeed required would then not apply. Fortunately, more and more companies start using “nice to have” in their job calls.
Were there specific hurdles that you had to master as a woman in your industry?
I’ve been mostly sheltered throughout my career, even during my Ph.D. studies in Hanover our professor aimed at getting a diverse group. After switching from academia to industry I had some cases of not feeling heard or having to prove my point more than others.
“Having to explain and mentor others pushes people to understand things on a different level …”
Is data science missing a female perspective?
This depends on the definition of data science if by data science one means only machine learning engineers then yes. If you think of data science as also data analytics then there are a higher women to men ratio.
Did you have mentors/role models?
I had a very “do the right thing” physics professor in high school, she always pushed her students to become their best self.
My quest for diversity was ignited by Ellen König, data scientist in Berlin, who posted on twitter last year that she was looking for a job in a company with active diversity efforts.
You are the founder of the PyLadies Hamburg. Why was that important to you?
We were really missing in Hamburg more meetups where people do things together. Our meetups are more workshop style where we meet and code together and one of us has to prepare a bit the material. Having to explain and mentor others pushes people to understand things on a different level than when just searching and applying it to solving a problem. We even have a couple of men joining and helping and participating because they like the topics.
Which skill set do you recommend to others who want to do data science?
Patience for exploratory data analysis.
Where and how do you keep yourself updated on current topics in the field of data science?
At the moment mostly twitter and the DataFramed podcasts.
What is your nerd and social skill factor?
Our sprints at work are Star Trek-themed… Because the data engineer team took Star Wars…
Ask yourself a question and answer it.
Do you get the Impostor Syndrome? Every day.
