We are the inventors of PyMC3, the leading platform for statistical data science. We have launched a consultancy to turn our expertise into your advantage. Our decades of experience in Bayesian modeling allows us to come up with unique and impactful solutions to your most challenging business problems.
Our team consists of PhDs, mathematicians, neuroscientists, and a former SpaceX rocket scientist. What unites us is our deep expertise and appreciation for Bayesian modeling as well as software design.
We work according to the following principles:
Bayesian modeling, as supported by our open source Python library PyMC3, allows you to solve problems that can hardly be solved any other way.
The tool and metholodgy are highly versatile and is being used successfully by various companies. For example, SpaceX used PyMC3 to optimize its supply chains (as explained in this blog post). But it's also being used to find planets outside of our solar-system, detect earthquakes, predict elections or estimate the spread of COVID-19 on rt.live. The PyMC3 journal article is the most cited paper on PeerJ where it was published.
Machine learning only cares about finding the solution that provides the highest predictive accuracy. The resulting models, however, are often impossible to interpret and investigate to determine whether a sensible solution was found.
Bayesian models on the other hand are hand-tailored to the problem structure they are solving. This makes the results inherently interpretable and can be investigated by you, the domain expert.
Another benefit of Bayesian statistics is that it allows you to incorporate the structure of your data into the model directly. This is different from machine learning which has to infer all structure from data -- there is no way to inform these models of structure ahead of time, which is why they require so much data.
When working with us we first gain a deep understanding of your data structure and the specific problem you want to solve. Together with you, we tailor a custom model that solves your specific problem and takes your unique data structure into account.
The resulting model requires far fewer data points than in machine learning. For example, many data sets have a nested or hierarchical structure that is impossible to map adequately in a machine learning algorithm, but is naturally supported by PyMC3).
We realize that you already know much more about your problem domain than we ever will. Our solutions do not replace that hard-earned knowledge. In fact, we can include this knowledge to inform the Bayesian model where to look for a solution -- and where not to look.
In order to quantify your domain knowledge we work with your team to calibrate the model by simulating data and asking how likely you think a particular pattern of data is to occur.
Classical data science approaches like machine learning usually just consider the single most likely scenario. Bayesian statistics, however, considers all possible scenarios according to the likelihood of their occurrence. This results in finding solutions that are robust across a whole distribution of scenarios, including tail-events.
If you meet one or more of these requirements, we are likely to add value to your business:
Problems in many domains meet these criteria as demonstrated by our customers in diverse fields ranging from finance, biotech, agriculture, pharma, adtech, supply chains and more.
If what we offer looks like a good fit for your team, please email us at email@example.com. We look forward to hearing from you.