Decoding the Data Scientist Hiring Gap
The need for AI/ML is growing and more and more jobs are being created as data awareness is increasing and more data is being collected. However, hiring data scientists has not been an easy task - most of these roles are not yet filled.
On the other hand, data science is a very popular discipline. There are many fresh grads as well as folks who have taken various courses online and want to transition to data science. Finding a job has not been trivial.
Looks like there is a lot of demand and a lot of supply. But there is a skill gap.
What exactly is the skill gap ? Lets see from the perspective of a hiring manager..
What are the main challenges when hiring for ML/data science roles ?
Have had the opportunity to have some interesting discussions with folks in the ML ecosystem in the last weeks on the gaps people see when hiring for data science positions. To keep the scope finite, when looking at beginner to mid level data scientists ( < 5 years experience overall),
The top gaps discussed so far can be broadly summarized as follows :
- Lack of problem solving skills in applying ML techniques to real products and making them work
- Lack of ML depth and breadth, though folks have worked on a few projects and write basic models
- Thinking from a customer/ business impact, and not just about model improvements
- Ability to dive deep into a problem, to figure out the real bottlenecks ML can solve
- Ability to deal with uncertainty and open ended problems as are common in real scenarios
- Humility to know what one does not know, and seek the right help and use available resources to solve problems the best way they can be solved.
- The desire and ability to stay with a problem long enough to make a meaningful impact
- Being receptive to adapting and evolving as the requirements and bottlenecks change
The best way to get these skills is to solve problems with ML for a real business with real customers for a while. However, it is also useful to understand if there are alternate ways to help people acquire at least some of these skills.
To decode the skill gap better, I am planning on diving deep into some of these aspects. I am having many more conversations with leaders trying to hire data scientists. If you are struggling with this problem, would love to talk to you as well.
If you have more thoughts, do leave a comment.
Corporate Trainer - LinkedIn & Recruitment Trainer ? Job Hunt Coach ?? LinkedIn Marketing - Helping MNCs | MSME | Startups | Job Seekers ? I’ll help getting High-ticket Clients & MNC Interviews in 90 Days
4 年This is true Lavanya Tekumalla
Principal - CX Architect, Infosys
4 年Excellent articulation putting the right thoughts
Senior MuleSoft Developer | API Development (back-end) | H1B
4 年Hello ma'am, I am facing this problem very badly in USA, and I would like get in touch with you to discuss and get some guidance from you regarding this. One more problem you did not address here is, a lot of people in industry have preference to hire PhD graduates for ML related roles ! **No Complaining** , but fresh uni grads like me are facing this problem heavily. Humbly request you to let me know why exactly this preference exists in market, and what are some counter measures MS - folks like me should do to deal with it. One more problem is, there are plenty of videos / blogs existing on internet regarding "Interview Experiences" of Software roles in product based companies. But the same are not there for applied sci and data sci roles at product based companies. Many fresh graduates are absolutely clueless on how many rounds happen and what exactly are tested in the interview for an applied sci or a data sci role. There are very few of them on medium.com and none on you tube.