Our Mission
The world of data science is evolving so fast that it’s not easy to
find real-world use cases that are relevant to what you’re working on.
That’s why we’ve collected together this knowledge from industry
leaders with practical use cases you can put to work right now.
Our Story
The role of data scientists, data engineers and analysts at financial institutions includes (but is not limited
to) protecting hundreds of billions of dollars’ worth of assets and protecting investors from trillion-dollar
impacts, say, from a flash crash. One of the biggest technical challenges underlying these problems is
scaling time series manipulation. Tick data, alternative data sets such as geospatial or transactional data,
and fundamental economic data are examples of the rich data sources available to financial institutions,
all of which are naturally indexed by timestamp. Solving business problems in finance such as risk, fraud
and compliance ultimately rest on being able to aggregate and analyze thousands of time series in
parallel. Older technologies, which are RDBMS-based, do not easily scale when analyzing trading strategies
or conducting regulatory analyses over years of historical data. Moreover, many existing time series
technologies use specialized languages instead of standard SQL or Python-based APIs.
Organizations constantly work on allocating resources where they are needed to meet anticipated demand.
The immediate focus is often on improving the accuracy of their forecasts. To achieve this goal, organizations
are investing in scalable platforms, in-house expertise and sophisticated new models.
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QUANTUM COMPUTING CHANGED EVERYTHING!
Oh well. I discovered quantum physics about 10 years ago and never stopped loving it. I think I'm fascinated about it each time I think about it, so I did a Master in Theoretical Quantum physics in Paris and then I moved to Vienna for my PhD. I'm still in Vienna now and I did a PhD in the field of quantum optics, and this was very fundamental research. So fundamental is by opposition to applied meaning it's research for let's say the sake of understanding. So, you always hope that there's going to be an application in the end but that's not what drives you and it's quite remarkable now because I'm making a transition now. So, I'm working at Machine Learning Reply where I'm working on very specific use cases, so for example, the BMW Quantum Challenge.
