There have been a fair number of articles on why data scientists are leaving their jobs. Most have a basis in Jonny Brooke Bartlett’s article from March, 2018. Some are in the ‘trade rags’ and a couple in more general publications like Forbes®. They all seem to share a common theme, they aren’t really about ‘leaving’ but rather about creating an environment that’s conducive to what data scientists believe they will be doing vs. what they actually end up doing. A couple of them have even offered guidelines to help employers have success with their data scientist teams and slow if not stop the ‘churn.’
Data Science has attracted interest for a few reasons, not the least of which are the potential big salaries. With an average starting salary of $95k, and some Wall Street firms paying as much as $1mil for data scientists who can generate powerful insights from their deluge of financial data, it can be a very lucrative career.
Another reason Data Science has been so attractive is the concept that it’s a lot of highly skilled people ‘geeking out’ focused on solving complex problems. The concept of using your skills alongside like-minded and equally highly skilled individuals to create a major disruption and influence the future is an ideal work environment for a data scientist.
Unfortunately, while richly paid, data scientists are poorly understood. At the intersection of reality and expectations, both the data scientist and the company are often sorely disappointed. The data scientist expected to be doing important work solving complex problems using cool machine learning and algorithms. Companies may more be expecting someone to be the all-seeing, all-knowing solution to any data question or challenge.
For the last several years companies, both large and small have jumped aboard the hire a data scientist bandwagon. But in reality, many companies are not actually prepared to bring a data scientist on board. Frequently there is an expectation that the data scientist will be the ‘go-to-guy’ for all things data. They are expected to be both a database and analytics expert with a ready report for every request. Many if not most companies are not able to use the data they have, up to 80% of a data scientists time is spent just scrubbing and organizing the existing data. Without a suitable infrastructure the company faces a ‘cold start’ problem.
To successfully build the needed infrastructure companies need to start by hiring a senior or expert data scientist. These experienced data scientists can both build the needed infrastructure and create a plan to scrub and organize the existing data. But in many cases, perhaps in an effort to save money on salaries they hire a junior level individual who is not experienced enough to build the infrastructure and does not want to spend all their time organizing and scrubbing data.
As you can well imagine this is where the disconnect starts. The data scientist, eager to solve complex problems can find him – or herself simply doing clean up, not the exciting things they expected. And the company eager for actionable data that can solve problems find themselves disappointed as well. In order to stop this mass exodus of data scientists we must reset the expectations on both sides of the equation.
Data scientists must find companies whose vision and goals align with their own critical path and key goals. Even then they may still need to recalibrate their expectations, to fit with where the company is in the data management process. It may be more valuable for a junior data scientist to start with a company that has more senior people already in place to help them acquire the experience needed to take on some of the more challenging opportunities.
Across the table companies must also reset their expectations to align more closely with reality. Hiring a junior data scientist is not going to get you the experience you need to build your infrastructure. A more successful plan may be to first hire a senior level person to design the infrastructure and then hire a junior level person to learn from that person. Working together they can put an infrastructure in place, clean up the data and actually supply actionable data and analytic reporting.
In today’s market there can be anywhere from 20-100 candidates for every entry level job. And pros with industry knowledge are always needed. As Jonny Brooks-Bartlett states “I love the job and I don’t want to discourage others from aspiring to be data scientists because it can be fun, stimulating and rewarding.”
GATE has more than a decade of experience in the technology field finding and filling temp, contract, and permanent positions. If you want to take the next step in your career, whether its data science or any other tech field, we are ready to help you. Give us a call at 877-369-GATE.
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