Data Scientist

data scientist

You don’t want to waste time cleansing data without knowing whether it will be useful?
You want to spend your time analyzing meaningful insights rather than extracting them from raw data?
You need to add a new data source to your analysis but don’t want to have to start again from scratch?
You’re looking to capture reliable signals early so that you can then improve on them?

You need to deliver a list of customers that are likely to churn tomorrow within the next two hours? Real-time predictions are not an option?

Look no further: is here to help.



With, non-parametric Machine Learning empowers you to fully focus on your business issues. Leverage the power of its algorithms to explore more data and deliver more timely and reliable predictions.

Focus more than ever on your interactions with business stakeholders and less on algorithm parameter tuning and deployment intricacies!

Looking to extract engineering features from relational data sets?
There’s an algorithm to assist you with that!

Looking to filter out non-informative features early in the process?
There’s an algorithm for that.

Wanting to combine features to get a predictive model?
There’s an algorithm to assist you there as well.

Wanting to stack models to boost predictive performance?
There’s even an algorithm to assist you with that.

Deploying models for real-time predictions?
Yes, you guessed it: there’s even an application for that!

With non-parametric algorithms naturally taking care of the over/underfitting trade-off, your imagination is the only limit. You can recode categorical features and use them as a more reliable input for your own modelling algorithm.
Or you can apply the optimal discretization algorithm to calibrate your existing predictive model. Alternatively, you can detect whether features are drifting over time and decide to include them in your analysis based on the results of a statistical test.

And it’s as quick as performing a sort on the data and never requires more than standard computer hardware.