- The soft and hard skills that are required by data scientists today
- Recent developments in AI and ML
- Staying up to date with the latest in data science
- Inspirations from Women In Tech
A person should believe in themselves, and in that practice makes perfect.– Olga Ivina
At what point did you realise that you wanted to pursue a career in data science (data & AI), and how did you get into it?
When I was 18 years old, I started studying mathematical statistics at the university. Of all things mathematics, this subject was the one that made the most sense to me. And then, at the time of my studies, the choice of work was between financial mathematics (quants, actuaries) and what was broadly called “analyst”. The latter dealt with data analysis in virtually any industry, and I believe it was a predecessor of modern data science.
My first job happened in 2006: I was working for a small consulting company focussed on movie analytics, and our team was developing a method to estimate the popularity and box office revenue of motion pictures about to hit big screens. Immediately after, I worked for a telecom company on a use case that is still relevant today – churn prediction. This is how the fun ride into professional data science started.
What skills and attitudes do you look for when hiring data scientists?
Check out what books helped 20+ successful data scientists grow in their career.
I work in consulting, so my requirements are specific, and they might vary from those elsewhere. For a data scientist in consulting, customer-centricity is key. We need to be ready to meet the customer at any point of their AI journey and help. A prospective candidate should be willing to explore together with the customer, and to go above and beyond to generate value to the people, and to the business. This might sound obvious, but it implies accountability, resilience, discipline, and a strong focus on collaboration.
To put this into perspective: data scientists are scientists, they are curious, thinking, seeking answers, and it is not easy to make the magic happen within the time, budget, infrastructure, etc. constraints, and assuring that the communication flow, which is vital, is constant and correct.
In highly selective environments, we also expect that the candidates bring the hard skills with them. Data science training is exhaustive, and it is akin to the one a medical professional has to undergo. Therefore, the preparation is lengthy, and there are no two ways about it. Nonetheless, passion and a growth mindset help get through. A person should believe in themselves, and in that practice makes perfect. I believe that the hard skills and knowledge required to pursue data science are exactly that – skills and knowledge, – and they can be acquired through self-education, experimentation, and learning from mistakes.
In your opinion, what have been the most relevant breakthroughs in data science impacting our world in the last 1-2 years, and what trends do you see emerging going forward?
In my opinion, the developments in responsible and ethical AI and MLOps are very important. In a longer perspective – definitely, the cloud, and the enormous advantages it gives in terms of computation, collaboration, tools, processes, and security. Recent progress in natural language analytics and generation, and in computer vision, is fantastic and noteworthy, as well.
What is one book that you would recommend young data scientists to read?
There is no one book that I could recommend to all: everyone should tap into their own source of inspiration, and it might have little to do with mathematics, programming, cloud, or – broadly – AI. The Harry Potter saga was that source for me personally.
How do you keep current with the new developments in the data science and AI arena? What are the top 3 resources that you use?
I am subscribed to several reputable outlets on social media, but, most importantly, I speak to people. And people kindly recommend materials for further learning.
What advice would you give to other business leaders who would like to step into realising data science use cases?
Data scientists are scientists, they are curious, thinking, seeking answers, and it is not easy to make the magic happen.– Olga Ivina
Can you talk about one or two women in tech who have inspired you to grow in data science?
There are many women in technology who inspire me. Sheryl Sandberg is definitely a reference person for many women in a multiplicity of industries. Maryam Mirzakhani was someone who definitely impressed me forever. Microsoft Consulting Services CTO Sarah Mocke is a person I look up to. And so is my former boss at Microsoft, May Nassif: she taught me a lot about leadership.
There are plenty of women I have worked or am working with who inspire, impress and lift me constantly: Agrita Garnizone, Trang Nguyen, Marie Standl, Evgeniya Ettinger… and the list goes on and on.
I am the Co-Founder & CTO of Xaltius, a Singapore based Data Science and AI startup, and a machine learning enthusiast who loves to interact with people and learn more about how artificial intelligence is shaping the lives of organizations and people and how it is being used to optimize business operations today.
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