How To Identify A Good/Bad Data Scientist In A Job Interview?
Bernard Marr
?? Internationally Best-selling #Author?? #KeynoteSpeaker?? #Futurist?? #Business, #Tech & #Strategy Advisor
Data scientists are in notoriously high demand, so when your company is ready to make the leap into big data, it pays to understand how to tell if you’re getting a good one.
Because of the vast amounts of money at stake with some big data projects, every data scientist wants you to believe that he or she is the kind of genius that can tease industry-changing information from a set of numbers and some code. And some can. But some can’t.
If you’re ready to hire a data scientist for your project or organization, there are some important questions to ask to make sure you get the right person for the job:
Does the candidate have solid programming skills?
A data scientist needs the skills to not just view and analyse the data, but manipulate it as well. A statistician who reviews and interprets a set of data is very different from a data scientist who can change the code that collects the data in the first place.
Do they excel at producing analytics for computers or humans? (And which do you need?)
There are two main types of big data analytics: those whose end user is solely a computer, and those whose end user is a human. If your end result is a machine learning algorithm to, for example, choose which ads to show on a website or make automatic stock trades, your analytics are for computers. If, on the other hand, a human will make a choice based on the analytics, your analyst needs a different set of skills, chiefly, being able to tell a story through data and providing good visualization of that data.
Can they provide concrete examples of when they’ve improved a business process through their work?
As with any position, you hope to see real-world examples of when they successfully implemented improvements to a business process.
Are they a good communicator?
Stereotypes would have us believe that it’s OK for scientists and techy types to be introverts with poor communications skills, but that’s not really an option with a data scientist. He or she needs to be able to communicate effectively with people who don’t “speak the same language,” tell a story through data, and use visual communications effectively.
Can they be creative and open minded?
Big data is a rapidly changing and expanding field that requires a certain open-mindedness and creativity. To innovate, a good data scientist must be able to look beyond what came before. If a candidate has implemented the same processes or procedures at multiple companies, ask yourself seriously if he or she is able to innovate and try something new.
Have they got a scientific mind-set?
As the name suggests, data scientists should be scientists that apply the scientific model to data. This means being able to experiment with data to find models and algorithms that are useful for businesses and can be used to predict future events. Scientist are inquisitive but follow the scientific method in their endeavour to find models that are useful in the real world.
Do they have solid business understanding?
It’s one thing to understand the science and mathematics behind analysing huge data sets. It’s another thing entirely to truly understand how that data affects profitability, user experience, and employee retention — or any of a myriad other factors important to the business. Someone with a background in business will be better at spotting trends that will benefit your business.
If you are a data scientist or have hired one for your company, what other traits would you add to the list? What differentiates a good data scientist from a mediocre one? I’d love to hear your thoughts in the comments.
Thank you for reading my post. Here at LinkedIn and at Forbes I regularly write about management, technology and the mega-trend that is Big Data. If you would like to read my regular posts then please click 'Follow' and feel free to also connect via Twitter, Facebook and The Advanced Performance Institute.
Here are some other posts from my Data Science Central column:
- What is Big Data (Infographic)
- Big Data In The Amazing World of Gaming
- Is Microsoft Putting Big Data At The Heart Of Their Business?
- The 5 Vs of Big Data (Infographic)
- 4 Ways Big Data Is Transforming Healthcare
- Smart Big Data: The All-Important 90/10 Rule
- That's Data Science: Airbus Puts 10,000 Sensors in Every Single Wing!
- From Farming To Big Data: The Amazing Story of John Deere
About : Bernard Marr is a globally recognized expert in big data, analytics and enterprise performance. He helps companies improve decision-making and performance using data. His new book is Data: Using Smart Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance. You can read a free sample chapter here.
Scientific Computing Scientist in Mathematics-Statistics | Machine Learning | Causal Inference | Implementation and Development in Data Science
5 年Let me put this clear: If you are going to sort out people based on few prefabricated questions without context, you going to have a hard time to find a candidate. Imagine if I ask you what X means, out of context; Do you think that question will click in the cadidate head? Context is everything!! Sorry?Bernard Marr but your statement "A statistician [...] is very different from a data scientist who can change the code that collects the data in the first place" shows you don't know what a statistician does at all. I suggest you to revise it.? Ah, take a look at these posts: https://www.dhirubhai.net/feed/update/urn:li:activity:6587807898519289856/
Profesional en Ciberseguridad con énfasis en estrategia, innovación y psicología social
9 年Scientific good, or bad data. The question is not whether it is pure analyst to study any career statistics and then passed to the analysis of data and BigData programmatically in R, or by statistical quantification of public or social policies. The scientific data, may not know much of computer science, but it specializes in the tools you need, and do not necessarily have to be worse than one that controls all computer languages. As in Working Groups Manager, Human Resources Manager, I learned that it is much more important intrinsic motivation to the worker, the academic preparation. It's just my opinion.
Director, of Education Evaluation and Data Analysis and Clinical Associate Professor at Claremont Graduate University
9 年Ben, I've been thinking a lot about your post and reflecting on several of the comments. In my management experience I've always looked for 'data scientists in the making.' I could hire a terrific programmer who could manipulate data, build DBs, learn the latest visualization software and add business acumen and industry scanning abilities and have a data scientist. I could also hire social science, public health or business administration major add coding and visualization skills also get a data scientist. I have found that to take an ambiguous executive question and move it through analysis, insight and back to executive action takes a well-functioning team. My point is we are growing these people right around us and we need to see the potential not necessarily wait for the perfect package.
Sr. Director, Cyber CISSP MBA PMP MCSD
9 年For those of you disagreeing with Bernard in regards to #1 - I don't understand why you see that as such a high bar to achieve for a proficient Data Scientist that can contribute. If they're truly experienced and haven't considered at least simulating and testing their theories via programming I would have to wonder about their abilities.
AI trust, governance, safety @ ServiceNow
9 年For the most part, I agree. But I disagree with the inclusion of 'solid programming skills', especially the way you describe what you mean by that. You suggest that a data scientist must be able to "change the code that collects the data in the first place." But why is that more important than, say, having the skills to design how research should be done and how data should be collected and analyzed? In the context of building a new house, we recognize the importance and value of having architects, engineers, contractors, tradespeople specialized in masonry, electrical, glazing, roofing, and so on. An excellent architect should be able to bring a solid understanding of materials and grade and climate to bear on their design work, but we wouldn't expect the architect to "change the code" that collects the materials in the first place. The same is true in many of the most specialized research laboratories in the world -- leading scientists in those labs need not be experts in all of the specific methods used to collect and manipulate data used in experiments and studies. In my mind, what is more important for a so-called data scientist to have is solid skills in scientific and analytical thinking plus experience using some range of research methods. Do they need to be a Hadoop expert? No because Hadoop will only be relevant in a small sub-set of research processes. Of course YOU might want a data scientist with Hadoop skills because you have problems that require those skills, but that's hardly the same thing as saying that all data scientists need those same tool- or framework-specific skills. To suggest so, in my mind, betrays a mis-understanding of the scientific mindset and a mis-alignment on using research to answer the most important questions that arise.