EP26 – Data Scientist : Jeremy Achin

Hello People,

Here is the fourteenth article of my series concerning famous Data Scientists. We will be focused on Data Scientist who turned Entrepreneur. Today we will talk about Jeremy Achin. From Travelers Insurance to founder of DataRobot.

So we will follow the following steps.

(1) Who is this Data Scientist?

(2) What is his work & Entrepreneur project are about?

(3) How can I benefit of such learning insights in my career path & theme selection?

Who is this Data Scientist?

Jeremy Achin is an American data scientist who turned entrepreneur in 2012 when founding DataRobot. Initially being a graduate of  University of Massachusetts, Lowell, Achin studied Math, Physics, Computer Science, and Statistics. While going entrepreneur, Achin is now passionate about helping organizations become more efficient by deploying machine learning everywhere. It could be said that Achin is a  true data science enthusiast, as he spends his spare time building predictive models, mostly dedicated to data science on Kaggle.com.

What is his work about?

Achin, Prior to DataRobot, was Director of Research and Modeling at Travelers Insurance where he built predictive models for pricing, retention, conversion, elasticity, lifetime value, customer behavior, claims and much more. Being a number cruncher and a refined analyst he is among those who allowed Data Science to become the Sexiest job of the 21st century. However, as an expert he also put in light that fake Data Scientist are roaming. As he mentions it in the article of the Boston Globe:

“I saw a lot of people on LinkedIn changing their titles to data scientists — like, rapidly. But it’s clear that some of these people are not data scientists and can’t actually do the job.” –  Jeremy Achin

Being already in the data field and strengthen by this strong belief; Achin opened Data Robot in 2012, and gathered skilled Data Scientist (mostly kagglers) in order to continue his ground work as a prolific one; while giving real insight about how the job is to be done.Indeed in founding DataRobot, Achin worked mostly on data related analysis in order to foster problem solving and generating predictions.

DataRobot: The company offers a machine learning platform for data scientists of all skill levels to build and deploy accurate predictive models in a fraction of the time it used to take. The technology addresses the critical shortage of data scientists by changing the speed and economics of predictive analytics. The DataRobot platform uses massively parallel processing to train and evaluate 1000’s of models in R, Python, Spark MLlib, H2O and other open source libraries. It searches through millions of possible combinations of algorithms, pre-processing steps, features, transformations and tuning parameters to deliver the best models for your dataset and prediction target.

How can I benefit of such learning insights in my career path & theme selection?

Achin is inspiring, as the latter is before hand a numeric master and indeed the heck of a good Data Scientist. Being  concerned about problem solving and related business analytics to enhance the use of data, he is among those who use Data Science in order to generate predictions while forecasting solutions. Later on, while making a talk about “Black Boxes and Unicorns“, he got me pondering on the real use of data and related regression models. The fact that he uses such assumptions in the Boston Globe article got me thinking that I do totally agree with him. Because, lots of people are taking on the title while not getting  the related skills and bearing in mind that the journey to reach the goal is quite long!

This remembering me a great caption from Dr.Eric Thomas:

“There is no shortcut to Success! Did you gave in the time it needed?”

Achin is to be considered as a role model. Considering that he also embrace the goal of generating more and more real Data Scientist by changing the game rules with DataRobot and shortening the learning curve time. To encapsulate my point I would only say that the latter goes from data to analysis, then from analysis to insights and from insights to predictions while creating tools to enhance deliveries and learning curve. This being in total alignment to my future goals in the data field.



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