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!
Most companies collect a huge amount of data during interactions with their customers. These days, this is true across most industries. This ream of data creates the opportunity for data-based optimisations of these interactions.
I am passionate about helping businesses derive value from this data by optimising customer interactions. From experience, I know that there is no single right approach to doing this. So I work with companies to derive and implement customised optimal policies. My particular expertise centres around modelling customer behaviour and developing pricing policies.
How did you start out in your tech career?
When I finished my PhD, there were not really any economists in data science. So I took a job in litigation consulting in San Francisco. That work was centred heavily around causal inference with observational data, especially in the context of pricing policies; thus quite related to data science.
After a few years, I was getting restless and started to explore what might be next for me. By that time, the idea that economists made good data scientists had just started to come up in Silicon Valley. One of the first companies to realise economists’ potential was Uber. I applied and got the job!
What are the signs of success in your field?
A data scientist needs to be able to create business value with their technical solutions. A successful data scientist is able to do this repeatedly, thus demonstrating that they have both a good understanding of the business and its pain points as well as the ability to bring their technical expertise to bear on these problems.
What is the best and worst thing about your job role?
The best thing is definitely the fact that a senior IC is (should be!) fairly flexible in the problems they attempt to solve. This flexibility affords me the opportunity for continued learning.
There are not many bad parts about this role, as far as I am concerned. One of the trickier ones to navigate is striking the right balance between developing technical solutions to the problems one has chosen to solve and communicating solution concepts to non-technical stakeholders. Both are important and take a lot of time, and it is not always easy for me to find the right balance that keeps the project on track.
What can you advise someone just starting out to be successful?
One way of thinking about the role of data science within a company that I sometimes find helpful is as a consulting function: data scientists need to identify problems that can be solved with data science solutions and then convince the relevant product owners or stakeholders that these solutions will actually improve the business’s bottom line.
With that mental model in mind, I think there are two skill areas that stand out: the ability to develop and implement technical solutions and the ability to communicate these solution concepts to non-technical audiences. Both are important, but the way they can be learned differs.
Communication skills can best be learned on the job. Working with more senior colleagues and using any and all opportunities to present one’s own work is a great way of improving communication skills.
Technical skills, on the other hand, are hard to teach on the job. Being successful here means reading and learning technical skills outside of work. One area that many data scientists tend to neglect – but that I think is absolutely crucial for success – is understanding the statistical foundations of data science. With a good understanding of these, a data scientist is able to develop solutions that will actually stand the test of time.
How do you switch off?
I love cycling. For me, there is nothing like going cycling for several hours in order to clear my head!
What advice would you give your younger self?
I have found data science a very rewarding field. I think my advice for my younger self would be to accept a little more career risk and give that emerging field of data science a chance a couple of years earlier than I ended up doing.
What is next for you?
I am quite happy where I am right now and will focus on that role for the foreseeable future.
If you could do anything now, what would you do? Why?
Solve the climate crisis and bring about world peace! I love data science but if I could do anything, I would solve some real problems.
What are your top 5 predictions in tech for the next 5 years?
If only I knew! Narrowing it down, however, I may venture a thought on the evolution of the field of data science. I believe that the field will continue its development towards professionalisation. I believe this will partly be driven by the fact that we will see more and more software solutions that will make the daily life of a data scientist easier along the whole model development and deployment chain.
In my view, this will mean that theoretical skills will become more and more important. If we can easily estimate and deploy complicated solutions, then those that understand their uses most intimately are the ones that will make the right choices for the problems at hand repeatedly. And repeatedly making the right calls is what will lead to long-term success.
At the same time, less time spent on model development and deployment also means more time that can be spent on identifying problems within the organisation that can be solved with data science solutions. I believe we will continue to see the pressure for data scientists to convincingly prove the value of their contributions in terms of improving the bottom line of the business.
Watch Matthias’ 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!