Data are becoming the new raw material of business
The Economist


The 3 Things That Make Technical Training Worthwhile

Managers understand that having employees who understand the latest tools and technologies is vital to keeping a company competitive. But training employees on those tools and technologies can be a costly endeavor (corporations spent $130 billion on corporate training in general in 2014) and too often training simply doesn’t achieve the objective of giving employees the skills they need.

At The Data Incubator, we work with hundreds of clients who hire PhD data scientists from our fellowship program or enroll their employees in our big data corporate training. We’ve found in our work with these companies across industries that technical training often lacks three important things: hands-on practice, accountability, and breathing room. Continue reading

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What Kind of Data Scientist Do You Need?

An article written by Data Incubator founder Michael Li was featured on Harvard Business Review today. It can be found where it was originally posted here.

 

If you’re looking to hire a data scientist to join your company, you’re not alone. At The Data Incubator, we work with hundreds of companies that are looking to find data scientists from our Fellowship Program. In our experience, candidates usually come from one of two disciplines: computations or statistics.

Candidates with a strong science or math background usually have had rigorous statistical training in distinguishing between signal and noise and can tell when they are “overfitting” a complex model. Those with a computer science background frequently have the software engineering chops to handle large amounts of data by taking advantage of parallel and distributed computing. While all data scientists need to be functional in both, we’ve found that people coming from each of these backgrounds have quite different strengths and weaknesses. So which type of background should you look for when hiring? That will depend on your business — and whether you’re hiring for a digital or non-digital department.  Continue reading

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The Enterprisers Project features HBR article: Retaining your data scientists

Yesterday, The Enterprisers Project covered Michael’s Harvard Business Review article “Retaining Your Data Scientists.” Take a look at their coverage below and at its original source.

 

It is no surprise that data scientists are in high demand. “McKinsey estimated that we will be facing a shortage of 140,000 to 190,000 data scientists by 2018.”

Managers facing this shortage are going to need to be creative to retain the data scientists they have. In this Harvard Business Review article, Michael Li, a data scientist who has worked at Google, Foursquare, and Andressen Horowitz, and has held the executive director position of The Data Incubator, provides many ways companies can keep their data scientists motivated.

One example is allowing data scientists to install and use their favorite tools. Michael says, “Data scientists don’t exist in a vacuum. They are part of a greater universe of peers collaborating in a large open source movement. Many of the tools are open source. So allow your data scientists to install and use their favorite tools.”

Michael has many recommendations to retain and excite talent. Read his article to learn about the three main categories of keeping these employees motivated: support, ownership and purpose.

 

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The Two Questions You Need to Ask Your Data Analysts

An article written by Data Incubator founder Michael Li was featured on Harvard Business Review today. It can be found where it was originally posted here.

 

Data scientists are in high demand. McKinsey predicts a need for 1.5 million new data professionals in the U.S. alone. As these droves of analysts join organizations, it’s critical that they know how to talk with managers about their findings. But the burden for good communication doesn’t just fall on them. For their part, managers – the consumers of the analysis – need to ask the right questions to be sure they understand the key concepts behind data analysis.

At The Data Incubator, we work with hundreds of companies looking to train their workforce in modern data analytics or hire data scientists from our selective PhD fellowship. Our clients often ask us how they should engage with their newly trained or newly hired data professionals. Here are two critical questions we suggest they ask when trying to understand the results of any data analysis. Continue reading

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The Best Data Scientists Know How to Tell Stories

An article written by Data Incubator founder Michael Li was featured on Harvard Business Review today. The article can be found where it was originally posted here.

 

When hiring data scientists, people tend to focus primarily on technical qualifications. It’s hard to find candidates who have the right mix of computational and statistical skills. But what’s even harder is finding people who have those skills and are good at communicating the story behind the data.

At The Data Incubator, we run a fellowship identifying the top 2% of STEM PhDs looking to work with our partner companies, which range from larger firms like Captial One or Genetech to smaller companies like Betterment or Yelp. Here are three attributes our partners look for in data scientists, and specific questions they use to identify those attributes: Continue reading

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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:

Data 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

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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:

When 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

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