Hiring an IT candidate is one of the hardest tasks that human resource professionals have to accomplish. Demand for IT professionals is greater than available individuals on the market, which produces competition between companies for the scarcely qualified developers. This can result in a lot of poaching of talent from rival employers. Take, for example, the career paths of some of the most famous IT professionals Ian Goodfellow and Guido van Rossum.
Goodfellow is one of the world’s foremost experts in deep learning and one of the creators of Tensorflow, perhaps the most powerful neural network libraries. After getting his Ph.D. he started his career with the Google Brain research team, later leaving this position to join OpenAI, only to be hired again by Google a year later. Two years afterward he was employed by Apple to his current position, Director of Machine Learning.
The case of van Rossum is somewhat similar. As the creator of the Python programming language, he was lured into work by the biggest IT companies in the world. He first joined Google for seven years, then was convinced to work for Dropbox for six more years until retirement. Even after retiring, he continued receiving juicy job offers. Eventually, he accepted returning to work for Microsoft.
What is exemplified in these stories is that even IT giants, such as Google or Microsoft, have difficulties and must navigate a highly competitive environment to hire the needed professionals. Moreover, this challenge is even greater for finding experts in Data Science, because this field demands a stronger background in academic and professional experience. Therefore, it is paramount to be very convincing when attracting data scientists to a new job. Let's start a brief introduction to the profiles that a data scientist could have.
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Although data scientists may have different academic backgrounds, such as physicists, economists, mathematicians, software engineers, or computer scientists, in practice they may have the same approach regardless of their study focuses. These approaches depend on their experience in the data science field.
Another aspect about their profile to consider is whether you should hire a data science freelancer or do you need to outsource a whole team.
Data science is an extremely broad field, applicable to hundreds of different contexts. Each one implies specific knowledge, both of the problem in question and of the mathematical and computational theory to answer it.
A general data scientist is someone who knows a considerable amount about all theories, the classical tools to apply in each context, and how a good solution could be faced. This person has had many works about different topics and areas over his career. A representation of this expertise is Andrew Ng, who has completed works about: Natural Language Processing, Clustering, Deep learning, Images, Robots, Time series, and more.
This profile type is good for a person that leads a data science team, works on multiple projects (at the same time or sequentially), or must create completely new solutions combining techniques from different areas.
On the other hand, there is a specialist, who knows everything about a specific area of Data Science. This type of candidate knows the latest and best tools and how to use them in this field. They are able to implement the best solution known and potentially further improve them.
For example, if you want to create a solution about Natural Language Processing, like a very sophisticated chatbot, a perfect candidate would be someone like Tomáš Mikolov. He is “famous” for working on NLP research for the last ten years and creating and implementing program frameworks able to understand free text and generate rational responses. However, for another kind of solution, like detecting objects with images, his implementation time will likely be too long and the solution won’t be very good. Most likely, he would reject such orders and search for other jobs more fitting to his skills.
The first thing you need to know is what kind of profile fits with client goals. Then, you can start your search and selection process without losing time in failed matching.
The first place many look to find Data Scientists is a classic job offer site, like Linkedin. These are not bad options, but there are more specific websites to gain better information about an applicant’s skills.
Once you’ve found viable candidates, you may also be interested in our article about how to interview and what to look for in a data scientist.
When you contact a candidate or post the job description on any job offer site, you should explain elements of the job such as commitment, job type (remote or on-site), company goals, task descriptions, technical skills or knowledge as well as technologies desired. If no technologies, knowledge, or skills are specifically desired, this must be stated additionally.
We provide you with a job description example, based on a job offer post of a popular international company, aiming to attract talent:
We are seeking an experienced data scientist to assist us in discovering information hidden in vast amounts of data, make smarter decisions, and subsequently deliver stronger company products. Your primary focus will be in applying data mining techniques, performing statistical analysis, and building high-quality prediction systems to be integrated within company products.
Skills and Qualifications
Additional information (optional):
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