Data are becoming the new raw material of business
The Economist

Two Types of Data Scientists: Which Is Right for Your Needs?

This week, Data Incubator founder Michael Li was featured on Data Informed. His article Two Types of Data Scientists: Which Is Right for Your Needs? can be found below and was originally posted here on July 16th, 2015.


arrow-2079315_960_720There’s a lot of good discussion about technology in big data, but not enough informed discussion about the talent in the field. We usually spend more time thinking about how to optimize our MapReduce jobs than we do thinking about how to motivate the data scientists who write them. We often use the term “data scientist” to encompass two very different types of roles: data scientists who produce analytics for humans, and data scientists who produce analytics for machines. It’s an important distinction, especially because the backgrounds and skill sets necessary for success in these two roles are quite different.

Lately, I have been seeing increasing awareness among employers of the importance of understanding data science and this division within the data science role. This certainly isn’t the only distinction among data scientists, but when it comes to formulating a successful big data strategy, it’s the most significant.

Here’s the difference and the kinds of backgrounds and motivations an employer can expect to look for in each type of data scientist.  Continue reading


KD Nuggets Interview: Michael Li, Data Incubator on Data-driven Hiring for Data Scientists, Part 2

Last week, KD Nuggets invited The Data Incubator’s Founder, Michael Li, in for an interview. Below, you can find part two of the article in full, which was originally posted here. Part one can be found here.

By Anmol Rajpurohit@hey_anmol

Here is second and last part of my interview with him:


Anmol Rajpurohit: Q5. What has been the feedback from hiring companies? 

Michael Li
Our hiring partners love that we’re presenting them with talented folks that have been pre-screened and evaluated technically and see us as complementing the traditional recruitment agencies they already work with.  They also appreciate the opportunity to network with our Fellows in an informal setting before deciding whether or not to set up an interview. data-incubator

AR: Q6. For the current PhD or Master’s students aspiring to be a Data Incubator fellow, what would you suggest they focus on during their degree program? 


ML: On the technical side, being a data scientist is about combining math and computer science.  Having a strong background in mathematics and statistics is what allows you to interpret your findings from all this data.  Having a strong background in computation is what will give you the tools necessary to manipulate all this data. 
Continue reading


KD Nuggets Interview: Michael Li, Data Incubator on Data-driven Hiring for Data Scientists

Last week, KD Nuggets invited The Data Incubator’s Founder, Michael Li, in for an interview. Below, you can find part one of the article in full, which was originally posted here.

 

We discuss the launch of the Data Incubator, its business model, why we need data-driven hiring, selection process for the incubator program and alumni feedback.

By Anmol Rajpurohit@hey_anmol

michael-liDr. Michael Li is Executive Director at The Data Incubator. Michael has worked as a data scientist (Foursquare), quant (D.E. Shaw, J.P. Morgan), and a rocket scientist (NASA). He did his PhD at Princeton as a Hertz fellow and read Part III Maths at Cambridge as a Marshall scholar.

At Foursquare, Michael discovered that his favorite part of the job was teaching and mentoring smart people about data science. He decided to build a startup that lets him focus on what he really loves.

Here is my interview with him:
Continue reading


Retaining Your Data Scientists

Yesterday, the Harvard Business Review featured an article written by The Data Incubator’s Co-Founder, Michael Li, on how to retain your highly sought after data scientists. Check it out:

business-1839191_960_720Data scientists are in high demand: McKinsey estimated that we will be facing a shortage of 140,000 to 190,000 data scientists by 2018. Employers have to be on top of their game to keep competitors from poaching their hard-won data science talent. At the The Data Incubator, we work with dozens of companies eager to hire data scientists from our fellowship program. But, as we explain to those employers, attracting and hiring great data scientists is only the first step: they also need to motivate and retain them. We’ve drawn up some best practices both from our own experiences as well as from those of our hiring partners. They fall into three main categories: Supportownership, and purpose. Continue reading


The Question to Ask Before Hiring a Data Scientist

Last month, the Harvard Business Review featured an article written by The Data Incubator’s Co-Founder, Michael Li, on how to approach hiring a data scientist. Check it out:

question-mark-2010011_960_720When hiring data scientists, there’s nothing more frustrating than making the wrong hire. Data scientists are in notoriously high demand, hard to attract, and command large salaries — compounding the cost of a mistake. At The Data Incubator, we’ve talked to dozens of employers looking to hire data scientists from our training program, from large corporates like Pfizer and JPMorgan Chase to smaller tech startups like Foursquare and Upstart. Employers that didn’t have good hiring experiences in the past often failed to ask a key question:

     Is your data scientist producing analytics for machines or humans?

This distinction is important across organizations, industries, and job titles (our fellows are being placed at jobs with titles that range from Quant to Data Scientist to Analyst to Statistician). Unfortunately, most hiring managers conflate the types of talent and temperament necessary for these roles.

While this isn’t the only distinction among data scientists, it’s one of the biggest when it comes to hiring. Here’s the difference, and why it matters: Continue reading