Data Science Interviews: causaLens on Hiring
An interview with Maksim Sipos, CTO at causaLens
This interview forms the second part of our ongoing ‘Data Science interviews’ series, providing more insight into hiring in the Data Science sphere to empower our community to get the roles they aspire for.
In the first part of the series, we interviewed Sebastian Kaltwang from FiveAI to learn more about the widely coveted British Startup FiveAI, what they look for and expect from candidates, some tips and current opportunities within their extensive teams.
For this addition to the series, we interview causaLens, a fascinating UK-based startup on a mission to create a machine that predicts the global economy in real-time.
Q1: Tell us about yourself
My name is Maksim Sipos and I am the CTO of causaLens. Prior to causaLens I worked as a CTO and as consultant at a number of world-class London, Berlin and Silicon Valley startups. And prior to those I worked at one of the most sophisticated hedge funds in the United States. My academic background is in Mathematics and Physics. I got my PhD in Theoretical Physics (Statistical Mechanics) in 2012.
Q2: Tell us a bit about causaLens
causaLens is high-tech startup based in London. We are building a machine that predicts the global economy in real-time utilising Automated Machine Learning for time-series.
We started in November 2016 and we are very proud that within less than 2 years we have managed to work with some of the most famous logistics providers, hedge funds, asset managers, Tier-1 banks and tech companies in the world.
Our founders have worked at some of the most prominent global hedge funds and scientific institutions.
Our culture is based on building a world class research and development team and maximising our efficiency in order to solve real world problems with cutting-edge technology.
Q3: What skills/competencies do you typically look for in a candidate applying for a role?
We are mostly interested in talented individuals who are interested in developing cutting-edge tech inside a small and efficient team. We also expect that our employees will have a positive attitude and a business common sense.
We are a startup so we expect cleverness and ability to get things done quickly and efficiently. We look for evidence of amazing technical skills but we can be flexible about the form of this evidence. For instance, top academic credentials are one way to show this evidence, but we will also consider outstanding ability to self-learn, hobby projects, etc.
Q4: How do you assess whether someone’s right for causaLens and its culture?
Before making an offer to a candidate, we invite them to spend a day in the office with us. It is super important not only to work together on real project but also spend time over lunch and discuss. In a CV we are interested in great academic and publication record, participation in innovative projects, hackathons and previous start-ups.
We also expect candidates to have a look at what our company does and spend some time thinking about what our product could look like, what technologies we could be using etc.
Q5: What level of preparation would you expect from a candidate?
On the data science side, we believe that the ability to write code and understand software systems is key to being productive. We expect an understanding of how computers work at a deep level and ability to write logical and clean code relatively quickly. Most candidates in our experience underperform in this ability.
We also expect a solid understanding of data science techniques and machine learning. We look for evidence that candidates can comprehend machine learning algorithms at a mathematical and intuitive level and can communicate this effectively.
We also expect candidates to have a look at what our company does and spend some time thinking about what our product could look like, what technologies we could be using etc. If they are a good fit for the team, we expect to have an interesting conversation and learn from our candidates as much as they are learning from us!
Even if you are coming from a quantitative field (like Mathematics, Physics, etc.), you need to spend the effort to learn the nomenclature of data science/machine learning.
Q6: Do you have any tips for aspiring Data Scientists?
Even if you are coming from a quantitative field (like Mathematics, Physics, etc.), you need to spend the effort to learn the nomenclature of data science/machine learning. Understand how practitioners think about problems and how problems are solved in practice. Understand how full data science systems work, from business use cases, data, modelling, to actionable insight and business impact.
Follow data science news/social media and the cutting edge. Make sure you understand the fundamentals of classical machine learning well before jumping into Deep Learning.
Deep understanding of computer systems is important (algorithm complexity, memory) as well as ability to quickly write code in a scripting language like Python or R.