After working 12 plus years in Mainframe support, leading end-to-end manual testing projects for an MNC, have resigned my job due to family reasons. Took 2 months to break, did research in Data Science(DS) and then started my journey in DS/Machine Learning/Deep Learning space 10 months ago, got a competency level of knowledge in ML, DL, Stats and Python. Have been attending interviews(so far 7 to 8) from different companies, few of them failed at telephonic rounds and few of them failed at F2F.
There are couple of reasons for failures..not being a fresher(Having 12 years of experience which is equal to a manager or Sr.Manager level)high and unrealistic expectations,lack of real-time experience, no proper mentoring, basic level of programming skills, average analytical thinker, the gap in service..etc. Am thinking to give up now and pick it up later(once I have some job in IT) and move on to related/less complex(comparatively ML/AI) technologies like Big data or PEGA or my old technology-mainframes or any other field(please suggest) where I can secure descent IT job in 2 to 3 months preparation.
I would want to try for another 3 months but am not very confident that will get a job in this field. Because this is a new industry, its ocean and not sure how situations would turn out after 3 months. So, thinking to pause my learning journey now. Once i have a monthly income, would want to resume(probably after a year)the journey in ML/AI space. By the time industry would be in much more matured.
At some point, I feel like, it may not be the right approach in continuing in ML/AI learning journey. Because of no monthly income and increasing gap in service.
Please share your perspective on my approach and also provide the best way to about from here.
Looking forward to seeing your response.
Thank You.
After working 12 plus years in Mainframe support, leading end-to-end manual testing projects for an MNC, have resigned my job due to family reasons. Took 2 months to break, did research in Data Science(DS) and then started my journey in DS/Machine Learning/Deep Learning space 10 months ago, got a competency level of knowledge in ML, DL, Stats and Python. Have been attending interviews(so far 7 to 8) from different companies, few of them failed at telephonic rounds and few of them failed at F2F.
There are couple of reasons for failures..not being a fresher(Having 12 years of experience which is equal to a manager or Sr.Manager level)high and unrealistic expectations,lack of real-time experience, no proper mentoring, basic level of programming skills, average analytical thinker, the gap in service..etc. Am thinking to give up now and pick it up later(once I have some job in IT) and move on to related/less complex(comparatively ML/AI) technologies like Big data or PEGA or my old technology-mainframes or any other field(please suggest) where I can secure descent IT job in 2 to 3 months preparation.
I would want to try for another 3 months but am not very confident that will get a job in this field. Because this is a new industry, its ocean and not sure how situations would turn out after 3 months. So, thinking to pause my learning journey now. Once i have a monthly income, would want to resume(probably after a year)the journey in ML/AI space. By the time industry would be in much more matured.
At some point, I feel like, it may not be the right approach in continuing in ML/AI learning journey. Because of no monthly income and increasing gap in service.
Please share your perspective on my approach and also provide the best way to about from here.
I understand your situation, and it is true that the hiring market usually does prefer to hire less senior professionals if they have the right skills and aptitudes for further growth. Apart from having done courses, more senior applicants for data sciences or ML jobs would be expected to have at least some amount of experience in these areas as well. Data science nuances require an overlap with either detailed business domain knowledge or a very good degree of proficiency in programming involving Big Data, including data manipulation and data transformation.
You are very right in placing an importance in not increasing the gap in your service. Since you need a regular income I would suggest that as a first priority you should aim to get back into the mainframe field so that your career continues.
I suspect that as a mainframer, your fundamentals in working with data, ie, data modelling, manipulation and transformation would be very good. If you need a little practice to become very familiar with them again I would recommend that you try to do some tasks that require hands-on work with data and basic programming for data manipulation so that you (re)gain confidence and sharpness in this area.
Beyond this, I see that you have two career options if you do not want to continue in the test lead role:
1. You could aim to become a Business Analyst. You mentioned Pega, so I suspect you have some exposure to CRM or the business processes in one or more industry domains. Select the industry domain that you have the strongest interest and skills in, rewrite your CV so that it brings out your business domain and process modelling skills, and start networking within your company with the intent of being given a chance to fill any available business analyst opening. In parallel, you could apply for internal as well as external job openings as a business analyst. Training in Pega implementations, if at all required, is likely to be given by your new team, although having at least some conceptual knowledge about it in advance would help.
2. As a second option, if you'd like to continue to aim for a career in data sciences, Big Data or ML, I would suggest that you pursue this objective as a lateral move within your company. The path to this would be through working with data and databases in your mainframe projects. From the testing role, if you can use any available opportunity to dig deeper into the database side of things within your own projects that would be a great start. Once you have a handle on working with data and also data transformations, you can start looking for openings in projects that involve working with Big Data. Your course qualifications may help give your application more credibility, and of course, you also have to rewrite your CV so that you present your experience with the highlights being on the tasks that would have sharpened your data skills.
Once you are in Big Data, the move to working with data science or machine learning may be a shorter step, in which you move your focus to data science programming that requires Big Data manipulation skills as a prerequisite. Moving to AI (working with neural networks and so on) may be the final step, if at all you want to go there. Whatever time it takes it will all be useful in strengthening your skills and improving your confidence, while still being employed.
It may take some time and patience, but once you have chosen your option and begun moving towards your end goal, you will get there. As I said before, it is also important to network hard at least within your company, so that you get to know of more opportunities and also get a chance to sell your own profile in informal settings. Attending external startup meetups can also help with networking and understanding where your capabilities stand with respect to others in the field. And, of course, all the while, you must keep looking out for new job opportunities that may appear on this platform or elsewhere.
1 Response
Please login to reply this post
Mario