How To Show Awareness Of The Wider Commercial Impact Of Data Science


How To Show Awareness Of The Wider Commercial Impact Of Data Science

When interviewing for a data science job you will have to show your programming abilities. You will have to show your math and stats abilities. You will also have to show your business expertise. It is this last step, "showing your business expertise", that will often trip people up. If you trip up on this last part of the interview, the feedback that companies will give you will be around your lack of awareness of the wider commercial impact of data science to their business.

What companies are really saying when they say you are "not aware of the wider commercial impact of data science"

This is the company nicely telling you that while you have strong programming skills and knowledge in data science, what you are lacking is specific knowledge of their business and industry.

The Data Science Venn Diagram

Given you are interviewing for a data science job, you have probably come across the The Data Science Venn Diagram.

In the diagram, there are three circles which represent a) hacking skills, b) math and stats knowledge, and c) substantive expertise.

The "Data Science Job" falls in the intersection of the three circles. So when a company says that you are "not aware of the wider commercial impact of data science", what they are really saying is that you were found lacking in the "substantive expertise" circle.

Why a company worries about a data science job candidate not being "aware of the wider commercial impact of data science"

A company worries about this because it means they will have to invest a great deal more into you than another candidate who many know more about their company and industry.

Conway writes in his The Data Science Venn Diagram article, "Science is about discovery and building knowledge, which requires some motivating questions about the world and hypotheses that can be brought to data and tested with statistical methods."

So when the company worries about your lack of "wider commercial impact", what they are really saying is that they didn't get the sense from you that you would be able to ask the right motivating questions about their business and wouldn't know the right hypotheses to make.

By not conveying these things to the interviewers, they came away thinking that they would need to teach you all about their business, business needs, and industry from ground up.

Which depending on the company, might be something they are willing or not willing to do.

A company will say that you "lack awareness of the commercial impact of data science" because you didn't demonstrate it.

Whether you have the knowledge or not, is almost secondary to you being able to show it within a data science job interview setting.

The whole purpose of the interview is for you to show the person interviewing you that you have enough knowledge and interest in the position that you'll be able to do the job incredibly well.

If your first thought is that the interviewer should just look at your resume, GitHub, or prior work, then sadly that will not be enough as interviewers are busy people and will default to the easiest behavior which is to expect you to spoon-feed them the information they need/want to know about you.

What you'd ideally like to do is to walk into the interview and get hired

You want them like you, see that there is a strong cultural fit, that you can do the job, that you understand the business environment, and that you can hit the ground running.

You want them to understand and internalize that you have a keen interest in the day to day business of the company and as such you'd be a very valuable asset to the team.

You want them to understand that you have an awareness for the business case of data science and not just the technical aspects of data science.

Most importantly, you want them to give you the job offer.

How to be great at showing your awareness of the wider commercial impact of data science

There are three steps to take going forward:

  1. Focus your data science projects and portfolio
  2. Start asking the right questions
  3. The Ramit Sethi briefcase technique applied to data science

Starting with step 1, "Focus your data science projects and portfolio", read through these two articles: a) How To Choose A Data Science Project For Your Data Science Portfolio and b) How You Should Create A Data Science Portfolio That Will Get You Hired.

The idea behind these two articles is that you should do data science projects that will expose you to the data and business issues of the company and industry you are interested in.

By doing this, you will be able to more accurately and easily discuss how data science fits into the role and company.

Additionally, this preparation before the interview, will give you a better understanding of the types of questions, scenarios, and exam projects that the interviewer will ask you to do during the interview process.

Continuing with step 2, "Start asking the right questions", when doing the preparation for the interview, start asking questions about how what you know about data science would fit into the company's world.

Some good starting questions to think about in regards to data science and what you could do with it within a company are:

  1. What problems does this company face?
  2. What is the strategy of this company?
  3. What products would be beneficial for the company to have?
  4. What services would be beneficial for the company to have?
  5. What would be the cost/benefits of pursuing the construction/deployment/support of any of these products and services?
  6. How would the company determine if the project was successful or not?
  7. How would the results be presented?
  8. Who would want to see these results?
  9. Who would be the stakeholders of these products or services?
  10. Why would these stakeholders back you and your product or service?

These questions will get you to start to thinking about the role, company, and industry and how data science can have a commercial impact on the bottom line.

Lastly, with step 3, "The Ramit Sethi briefcase technique applied to data science", you want to be able to accurately and convincingly get across that you've thought about how and why data science can help the company.

The idea behind the "The Ramit Sethi Briefcase Technique" is that you show up at the interview with ideas for what you would do if they gave you the job right this instant.

This forces you to do research on the company, think about what problems they are having, what shapes the solutions might have, and what they need to do in order to get there.

During the interview you would then bring this up and talk about what you would do and ask them for their thoughts.

This will show your interviewers that you a) get the industry, b) get the company, c) have ideas that they can build off of, d) that you are aware of how/where/when/why you will apply your knowledge in programming and data science to make the business more money, and e) that you really want the job.

So you can do this for each company and each interview.

In this way, whether you are interviewing for a junior, mid, or senior data scientist position, you will be able to showcase how much knowledge you have about the role, company, and industry.

It would be great if you had a large data science portfolio in which you had already done this work, but if that's not there, then at least have a good think about it to show that you actually cared enough to think about it.

The Next Steps: Trial and Evaluation

As you are going through your preparations for a data science job interview, make sure you do the above three steps (a) Focus your data science projects and portfolio, b) Start asking the right questions, and c) The Ramit Sethi briefcase technique applied to data science.

What you want to start doing is trying these techniques with every single business you interact with, especially the question asking and the "what would you do today if I was hired as a data scientist for this company".

This will help you expand your knowledge of the wider impact of data science in organizational settings outside of just the technical aspects of data science.

Then to evaluate what you are doing, start sharing your thoughts with others around you (and online) to get their feedback.

This will help you to start being able to accurately and deliberately communicate your thoughts to someone in a quick and easily accessible way.

To that end, good luck and best wishes!!


Do you want to become a data scientist faster than you think possible?
Get started today =>
Free 5 Day Email Step-by-step Instructional Course for getting started in data science


You might also enjoy these data science articles because you are terrific:

Sign up to receive the Data Science Weekly Newsletter every Thursday

Easy to unsubscribe. No spam — we keep your email safe and do not share it.