This was the goal. We break into the field. Build a track record of delivering successful Machine Learning projects. Grow our capabilities and value we bring to the business. Probably job hop once or twice.

Now we’re somewhere between year 4 and 10 of our Data Science careers. We are labeled Senior, Distinguished, Staff, Principal, or something else. After the glow of getting here fades, we eventually ask the question, ‘Now what?’

Companies are struggling to attract Data Scientists. They are coming to terms with monetizing Machine Learning products. They are transforming their business and operating models around the technology. Their focus is everywhere except for career path.

Businesses are just starting to understand what to do with Data Scientists. Most are a few years away from knowing what a career in Data Science looks like.

It makes more sense when you ask yourself, ‘How many companies have had a Data Science team for over 10 years?’ Not many. How many companies have Data Scientists who have been with them for over 5 years? Again, not many.

It is almost assumed you’ll leave after 2 or 3 years. I have actually heard a CTO talk about building Data Science career paths as a problem he wished they had. I had to laugh with him because he was being honest.

The business had started their Data Science team in 2016 with 3 hires. None of them were still with the company. Only 1 of the people hired to replace the original team was still there. They had already lost 1 of their 3rd round hires. In less than 5 years, they’d gone through almost 2 complete rounds of turnover and were starting their 3rd.

The argument is always the same. I say, ‘Career path is a big part of retention. Would you be more successful if there were well defined career paths for Data Scientists?’

Then they say, ‘I think my problem with retention is eventually Google offers $100K more than I can. Some startup offers more interesting projects, a CDS title, and equity. I’m fighting reality. I’ll create a career path once someone stays long enough to ask for it.’

It’s a self-fulfilling prophecy. Senior Data Scientists are forced to job hop because the business hasn’t matured enough to take a long term view on the role. We would need to create our own opportunities within the business and it’s a lot easier to find that opportunity elsewhere.

Eventually job hopping runs its course. Even at Google, once you’re in the upper level technical ranks, the thoughts on career path are something like, ‘Why would you want to do anything else?’ Most people eventually want to take that next step so even FAANG+ (MAANG+?) suffer from high rates of Data Scientist attrition.

It’s a complex reality and one that most of us will face. I made my own opportunity when I started my consulting practice. Personally, this is my 3rd go around with the senior plateau across 3 different technical tracks: SQA, Software Engineering, and Data Science. I have moved through the advanced technical, leadership, and strategy progressions. I build Data and Analytics organizations for clients, so I have built out career paths based on long term business needs.

This is the first post in a series on career advice for Senior+ level Data and Analytics talent. We face all the standard technology career challenges plus the uncharted career territory of an emerging field.

I’m going to talk about:

Common career challenges like promotions and raises
Your options with advanced or emerging roles
Leadership, strategist, builder, product, and technical leadership tracks
My experience starting a business and working with startups founded by former Data Scientists

I will explain the realities faced by my clients who are struggling with the flip side of Data Science career paths. Just like everything else in Machine Learning, you’re going to have to partner with the business to solve this, so you need to understand their point of view.

The best opportunities come from growing with the business over the course of 4-5 years. Obviously, not every business will survive or mature enough to work with you. I’ll talk about the signs of real and false opportunities.

High ROI Data Science

You’re A Senior Data Scientist…Now What?