A couple of weeks ago I was at two different business schools as a guest speaker. Both ISB, Hyderabad, and MISB Bocconi have specialized programs in business analytics and data science for working professionals. I gave talks about ‘career in data science & industry’s expectations from data scientists’. I got the opportunity to speak to more than 100 students / aspiring data scientists who have gone through the rigorous academic programs in data science. They were all just a few weeks away from their graduation.
First of all, I was really happy to see the enthusiasm among these experienced professionals to make their career transition to my field. I know from my first-hand experience that data science is one of the best professions to be in. For me, it is a sheer joy to find insights hiding in data.
All the students attending these programs were working professionals with an average work experience of close to 9 years. Now the common theme of questions from students was on the following lines
|1. Do these experienced professionals of other fields need to start afresh in their careers in data science like interns or freshers?
2. What is a good strategy for these professionals to start their careers in data science?
3. How will the industry perceive these professionals? Do they need to take a salary cut while making this transition?
4. How to prepare for data science job interviews?
As for the last question about data science job interviews, you may like to read the following articles
- Data Science Job Interview Questions and Preparation Strategy
- 7 Worst Mistakes in Data Science Interviews
In this article, I will tackle the first three questions about career transition to data science. Also, I plan to suggest a few pointers for a career transition to data science.
However, the objective of this article is to start a conversation among working professions who are planning a career transition to data science. Please post your questions, suggestions, experience in the comments section at the bottom of this article. I will also participate in this conversation to share my own experience based on your comments.
Career Transition to Data Science and Business Analytics
Let us accept that a career transition, like any other transformation, is not an easy process. This adjacent artwork by my favorite artist, MC Escher, captures the confusion of transformation (in the middle of the picture). Hence, it is no wonder that most experienced professionals making a career transition to data science from other fields are finding it full of confusion and difficulties. Luckily, data science is an extremely vast field with ample scope for most professionals to fit in. This allows professionals with distinct skills to find their niche within data science. I recommend you read the following articles to explore the wide varieties of skill-sets required in data science and ways to polish those skills: Career in Data Science- Play to Your Strength, & 6 Worst Mistakes for Data Scientists.
Let me share my 3 pointers that I believe will help you with your career transition to data science. I will also try to answer the 3 questions raised earlier in the article.
1. Managers & Leaders with Data Science Expertise
Data science is a relatively new field and is evolving extremely fast. Initially, data science teams used to have mostly specialized professionals with skills in number crunching and data management. Over the years problem statements for data science tasks have become much more complicated and interesting. This required multidisciplinary teams to come together and work on data science and business analytics projects. Business teams often work very closely with most of the high visibility and high impact data science projects. Domain experts are always an integral part of every data science project.
Additionally, for most competitive organizations leadership and board meetings are driven by data and facts. This trend is certainly pointing towards a future where managers and leaders need to have proficiency in data science to grow in their career. Now, there will be a direct implication of this for professional investing their effort in learning data science. These professionals considering a transition to data science can build data analysis as a powerful skill on top of their existing skill-set.
Hence, let me come back to the first question I promised to answer at the beginning of this article:
|1. Do these experienced professionals of other fields need to start afresh in their careers in data science like interns or freshers?|
My answer : no. I don’t think any experienced professional considering a career change to data science should think about starting as an intern. The idea is to add a new powerful skill on top of your existing skill set.
2. Look Around in Your Current Role
Now, let me try to answer the second question i.e.
|2. What is a good strategy for these professionals to start their careers in data science?|
I believe, a good strategy is to find / create a data science project in your current role and organization. This will certainly add a lot of value to your CV. Most professionals I have interacted with are considering a career / job shift when they are planning their transition to data science. This makes the career transition inorganic and I believe relatively painful. An organic career transition to data science is to add scientific and data-driven thinking in your current role. As I mentioned earlier soon every manager and leader are required to have skills and proficiency in data science, you can be the first generation of leaders in the same direction. In certain cases, you may still have to change your job / career to fulfill your data science dream but I suggest do this only after you have exhausted all the other options.
3. Find Your Purpose
Finally, let’s come to the third and last question:
|3. How will the industry perceive these professionals? Do they need to take a salary cut while making this transition?|
As I mentioned data science is a rapidly evolving field. The problem statements in data science are becoming highly multidisciplinary. This leaves a lot of room for professionals with different skill sets to collaborate and learn from each other. When we think of core data science skills it still involves quantitative, number crunching, and large data handling skills. However, the projects, that I and most data scientists work on, require creative thinkers with some domain expertise who can pose interesting and innovative business questions. I can tell you from experience, a professional with skills in defining innovative questions and solution approaches always sits on the top of data science hierarchy. These are usually the highest paid professionals. This is also the space where there is a dearth of talent. Now, for experienced professionals, this is the most lucrative space to fill.
These were my initial views on career transition in data science for experienced professionals. As mentioned the purpose of this article is to start a conversation between all of us to come up with appropriate strategies. So, please share your questions, suggestions, experience in the comments section at the bottom of this article.