I got my Ph.D in Machine Learning in 2008. Worked on the intersection of physics and supervised learning methods.Wasn’t happy as a postdoc and decided to join the industry instead.

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Joined a mid-level software company that developed analytics software for various businesses, hoping to utilize my ML and stats knowledge, but quickly found that at the time (2009~2010) domain knowledge and implementation expertise were much more valued than mathematical knowledge and modeling skills (again at that time – things seems to have changed radically since then – I remember hearing more than once while interviewing in 2009 hiring managers say that they’d rather hire a domain expert and train them on the science than hire a scientist and train them on the domain).

Within a year, I switched to a TPM/Architect type of role, going to client sites and doing implementations or assisting with project management and tech support, and worked for various consulting firms in that capacity, as there seemed to be more money and opportunity there.

In a way, I was never that far removed from data science, since the products I was peddling always involved an analytics/predictive function. But none of the roles I had involved any explicit science work, other than having to explain to clients and business stake holders the math that was behind some of the algorithms used in the software. My long term plan had always been to return to the science side of things at one point or another.

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In 2014, I realized that DS and ML were becoming fashionable, and that experience with neural networks (my old passion) was considered a hot skill on the market. It seemed like a perfect time to get back into the science side of things. I started learning R and Python (in my grad school days I was mostly a Matlab person) and catching up on the latest developments in the field, frequenting Kaggle discussions and other forums, practicing Leetcode, etc…

I thought the combination of business knowledge / consulting experience + Ph.D level understanding of the models and math involved would make me a golden candidate for various DS/ML roles.

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Instead, over the last 5 years, I have found it very difficult to break into the field, with recruiters and hiring managers rarely paying attention to me, and when they do, I can’t seem to get passed the technical screens or initial interviews because of my lack of real world experience with things like Spark, Flask, etc… and my mostly theoretical (very deep, but still purely theoretical) knowledge of how models other than time series and linear regression work.

Even more frustrating is that more and more people with less qualifications than me seem to transition at will into data science roles (business analyst or SDE completes a Coursera certificate in machine learning, gets promoted to data science role the next month).

To top it all off: I’m nearing forty now (which I gather is ancient in machine learning years) and I feel like my chances of breaking into the filed based on age alone are decreasing exponentially every year.

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Am I hopeless? Any advice for an aging data science has been/wanna be?