Six Things to do Before You Hire someone as a Data Scientist

Has it recently dawned on you that Data Science can rejuvenate your business in a number of ways? Do you feel left behind when you see competitors building data products embellished with wonders of Machine Learning and Artificial Intelligence? If you are not doing the same then you are justified in feeling left behind. Data Science is the reality of the 21st century. Closer are the times when the companies not availing and providing data services will be accused of living in the dark ages. Today is the perfect time to hire your first champion of Data Science.

However, what qualities would make this superhero the perfect choice you can make? Soon after starting the venture, you would realize that the way is not easy. It is not like hiring a software developer. There are multiple reasons. First, it is tricky to write the appropriate Job Description, as the horizons of Data Science are too wide. Secondly, too many people are applying and not all of them have required experience. The third reason is that industry standards and benchmarks available are very little in number.

In this post, we will tell you about six things to do before you hire someone as a data scientist.

1. Check if they can make basic version first

Roads of Machine Learning and Data Science overlap at many spots. You can think of them as cousins. Nonetheless, both hold the promise to change the outcome of your business fundamentally. It, however, is important to remember that your first data product should be more straightforward.

One appropriate example could be a business intelligence dashboard that can monitor the overall health of your business using key KPIs. The best policy would be to build something, which is simple but effective. Thus, you can easily convince higher management to invest more in your data science team. You can encourage them to take on challenging data projects.

Moreover, it is fundamentally significant to keep in mind that predicting outcomes for your business, using advanced tools such as machine learning only becomes reliable after multiple iterations and usually takes months if not years to build. It is a long way down the road.

So asking your candidate to build it would not be practical. If they can make a basic version, it means their basic concepts are clear. If their basic concepts are clear, they are a good choice.

2. Check if they can review your data properly

Everyone knows that data appears in various forms. The data your company produces or aggregates can range from texts to audio files, to images and even videos. In case your company is handling medical or financial records things can get even more complex. Often there are additional security measures and industry standards for storing and retrieving such data sets.

Ensure that your first hire has previous experience in handling those data types. An inexperienced person cannot review it properly. His/her lack of expertise will show up in no time when you will assign the task of reviewing old data. The opposite is also true. A candidate who reviews your data better than you can is the hero you are looking for.

3. Check if they can build Data Pipelines

Often the data produced by your core business is not adequately aggregated for use. Let us elaborate on one example. Large chunks of the data can be stored in a non-digital format. They can also be kept under lock and key in private digital storage. With the passage of time, data can get corrupt. On the other hand, maybe new data have a different data type. So check if your first hire has experience in scraping and collating data from multiple data sources. Also, ensure that he has expertise in building data-pipelines.

It will aid you in creating an operational skeleton for your business. This skeleton can be used to produce one or more data products. Well-built data-pipeline will also further enhance the quality of data security. It would ensure that new employees get restricted access.

4. Jack of All Trades is not the Champion You Need

Data Science is an umbrella term. The whole set of overlapping skillsets that spans from data preparation to artificial intelligence and data visualization, falls under it. Which software tool any data scientist uses, depends on his/her very own skill set.

For example, a veteran computer programmer will most probably know multiple coding languages such as Python, R, C++ and some big data frameworks such as Hadoop, Spark, and NoSQL, but he might not be very aware of the core concepts of machine learning. Scientist meanwhile may know a few programming languages, but he may understand advanced techniques such as MapReduce and machine learning.

Similarly, a person with a business background will know some of the coding languages and relevant software regulations such as SAS scrum.

Therefore, your first hire should be a ‘master’ engineer, not ‘jack of all trades’. He should be Picasso of his own art. If you understand the gaps in the expertise of your first hire, you can better plan a hiring roadmap for your data science team. It is often recommended to hire a data engineer first to build the data-pipelines, and then hire a business intelligence expert or a statistician to optimize the output.

5. Ensure they have Original Portfolio

A lot of candidates will show you a fantastic portfolio of what they have already built, during the interview. Things like a program that can automatically detect words or a chat-bot that can work as a virtual assistant are most frequently seen examples.

Keep in mind that many of these projects are available in open-source data science forums. So make sure that you clearly understand what the candidate has contributed to the project. Your mission statement should be “Hire for talent, train for tech skills.”

Very often, the first hire is a well-experienced software programmer who wants to make a career move into Data Science. As data science is an emerging field, it would be wise to encourage them to keep learning from forums, MOOCs or universities that offer relevant courses.

6. Do Not Prefer Purely Academics

Data Science is an extension of statistics. Here are the consequences of it. Many academic researchers from fields like economics, physics, mathematics, computer science, and engineering have jumped on the data science bandwagon to make a career switch to the fast-growing industry. However, some of the best data scientists come from universities and allied research laboratories, it is crucial to keep in mind that these specialists should be only be hired to optimize and not build your data product.

Data-Science is a blend of business, software engineering, and statistics. “A person who is better at statistics than any software engineer and better at software engineering than any statistician.” This is how some experts define a Data Scientist. Therefore, your first hired champion should possibly be a person with multiple years of software engineering experience, as well as some management experience. This way, it is assured that he can prioritize the tasks and deliver an impact for your business.

There are some things you have to do at your end before hiring. Here are the two of them.

1.Define Project Requirements

What do you actually want? What skills are needed to accomplish these goals? Furthermore, consider breaking down your project to smaller parts.

2. Write An Effective Job Post

Use the information you have summarized in the previous step to create a detailed and clear job post. Three things should be well defined. Targets, results and the estimated end date of the project.

In a Nutshell

Interpersonal skills of a person are the ultimate filter you need to apply. Scrutinize personality of your candidate, very carefully. No criteria are important than that. Hard work and ability to work under pressure is the must ingredient. The jobs in Data Science require mental strength. The crux is that Data Scientist must possess patience and critical thinking. He/she should be curious enough to get to the depths of data and conclude the accurate crux of it.