Sometimes we forget the humans behind the tech in our ever busy world. DSF is fortunate enough to know some incredible tech leaders across the world and has the privilege of hearing them present at our events. That being said, our Speaker Spotlight sets the stage to get to know our speakers on a more personal level and connect them with our growing community. Read the mini interview below!

A bit about Eyal:

Ex cosmologist turned data scientist. My superpower is simplifying the complex and turning data to ta-da!

My main drive is applying scientific approaches that result in practical and clear solutions. To accomplish these, I use whatever works, be it statistical/causal inference, machine/deep learning or optimisation algorithms. Being result driven I have a passion for facilitating stakeholders to make data driven decisions by quantifying and communicating the impact of interventions to non-specialist audiences in an accessible manner.

My claim for fame is that between 2004-2014 I lived in four different continents within a span of a decade, including three tennis Grand Slam cities (NYC, Melbourne, London). I currently work in Zimmer Biomet as a Lead Data Scientist.

I also write about my experiences in applied statistics in machine learning and data science on Medium: https://medium.com/@eyal-kazin

How did you start out in your tech career?

Prior to college my first professional roles were as a technical instructor of communication devices and then briefly a satellite technician. After that I backpacked South America and then started my undergrad studies in Physics, after which I returned to practice Spanish backpacking Central America.

My interest in data analysis started as a Ph.D student in New York University conducting research in cosmology where I was analysing distributions of galaxies to learn about the evolution of the Universe through its structure.

Following my Phd advisor’s advice to “go where the data are”, I accepted a Post-Doc research position in Melbourne Australia, and eventually left academia in 2014 when I realised the most interesting data is in the private sector.

My first data science role was in a consulting agency, which combined client facing and research. I found myself enjoying research more and moved into more research and product related individual contributor roles in biotech, health tech and currently medtech.

What are the signs of success in your field?

Signs of success are highly subjective.

For me personally having a job that is intellectually stimulating and provides a good work-life balance is ideal.

I feel that I’ve been fortunate, for the most part, in this regard in my trajectory so far.

Besides that I’m excited that as of last year I started to present in conferences research results for the first time since my academic career.

What is the best and worst thing about your job role?

The best aspect of my role is getting to learn new things and share with others.

The worst is that I don’t have a permanent office to go to. It’s an absolute privilege to be fully remote, but I’m midway the introvert-extravert spectrum and would creatively benefit from meeting people in real life two days a week.

What can you advise someone just starting out to be successful?

The best generic advice that I got from my grandmother was the importance of enjoying your work. When I was looking for my first role she told me that “You’re going to have to work for at least 50 years, you might as well enjoy it”.

For me that meant dedicating over a decade to studying and research in physics. Even though I decided to leave academic research I was fortunate to gain enough skills to have a fairly smooth transition to the private sector as a data scientist.

I also found that going to meetups are very useful to learn the landscape and make crucial connections.

How do you switch off?

The answer to that is divided into pre- and post- parenthood …

Before I had many hobbies which I enjoyed.

Now I have a so-called “second shift”, so that keeps my partner and I quite busy. When I get a chance, I enjoy reading, writing about my data science learnings on Medium (which isn’t exactly switching off, but rather changing mode …), cycling or finding something good to watch on (cliche warning …) Netflix.

What advice would you give your younger self?

Learn statistics and the science of decision making.

Specifically in regards to real life decision making: a mortgage can be a good thing.

What is next for you?

I think that I have a technical book in me. I’m currently working on a book proposal.

For now I’m exploring topics by writing on Medium. I have started to publish last year mostly about my experiences in applied statistics in data science and machine learning. Within less than a year  I’ve published ten articles and the feedback from the online publishers and readers has been very encouraging.

My profile is: https://medium.com/@eyal-kazin 

Highlighted articles:

Start Asking Your Data “Why?” — A Gentle Intro To Causality: A beginner’s guide to thinking beyond correlations.

Information Theory for People in a Hurry: A quick guide to Entropy, Cross-Entropy and KL Divergence. Python code provided.

Lessons in Decision Making from the Monty Hall Problem: A journey into three intuitions: Common, Bayesian and Causal.

If you could do anything now, what would you do? Why?

I feel that I’m in a good place at the moment. As I’ve said, I’d appreciate a permanent office environment two days a week.

If I had the opportunity I might consider trying out a different industry, e.g, the energy sector or a field related to applied physics.

But as long as I’m in a team that provides me with interesting challenges to solve, I’m a happy trooper.

What are your top 5 predictions in tech for the next 5 years?

  • AI models with (at least a basic) underlying understanding of the real world

  • The current gap in data literacy (in society and even amongst techies) will further broaden due to automated use of tools like generative models.

  • Drone and satellites will become more and more observable as clutter in Earth’s sky and low orbit

  • (Uneducated guess/wish): Crispr technology will be used for breakthroughs in medicine

  • (Uneducated guess/wish): Quantum computing technologies will have further breakthroughs in stabilising their qubits

Watch Eyal’s session at the Data Science Festival here.

Thank you to all our wonderful speakers for taking part in our Speaker Spotlight!

Want to become a DSF Speaker? Apply here!