Data scientists and ML engineers are some of the most sought after employees in the industry. Since these are young fields, expectations can get misaligned, leading to high turnover rates. In this article, we’ll discuss top reasons why data scientists are leaving companies, and what can be done to keep them aboard. We’ll also share advice for how to develop your team members.
Why Data Scientists Leave Companies
There are three main cultural reasons for leaving: work is not challenging enough, data models are rarely brought into production, and employees get demoralized by office politics.
First, data scientists may become disillusioned when the work they’re doing is misaligned with the original job description. When companies get job descriptions wrong, it’s not necessarily because they want to capitalize on the popularity of buzzwords, but because there’s a distinct lack of understanding of data science in HR departments.
This can be resolved by educating HR employees about specific roles so they can set the right expectations in job descriptions. Data science is a broad field, from hypothesis generation and causal inference all the way to deploying and monitoring. Many people associate the higher level work with data science; namely training, building and deploying a model. But a lot of effort precedes and supports this type of work, what is commonly associated with data analysis.
Second, data scientists will get frustrated when they spend all day working on models that never get deployed in the real world. While it is normal that a great portion of models are discarded because of noncompliance or bad performance, your company still needs a good system for achieving a good performance so the models can be deployed.
Software engineering has been maturing for decades, with many tools and methodologies in place to ship products and services. But data science and machine learning are new fields, and companies are still experimenting with project management frameworks. We’ve compiled a few popular methodologies in this article to help your team bring models to market.
Third, company politics is an often cited reason for leaving. Many data scientists come from an academic background, where job competition and gossip play less dominant roles. They might get frustrated with an office culture that rewards interpersonal games more than analytical and computational efforts. Managers can resolve this by creating a culture that rewards efforts, not outcomes. They should ask employees directly about their expectations for job growth, and to make sure their job interests are aligned with the company’s goals.
What Motivates Data Scientists To Stay?
We’ve discussed some reasons for leaving and what can be done to prevent this, but what else can leadership do to retain talent?
Compensation is one of the strongest ways to retain talent. Be aware that data scientists are in high demand, which drives up the salaries. This might make it difficult for startups to compete with large companies who can offer higher salaries and more benefits. But startups have other unique advantages over big corporations that allows them to recruit top talent.
Some engineers have been known to accept a lower salary to switch from a bureaucratic government agency to a private startup that’s disrupting their industry. They would happily take a pay cut to contribute to a meaningful product. Startups also bring the allure for early equity compensation that could explode in value in the future. Many millionaires have been created in Silicon Valley with this process.
Compensation momentum might be even more critical to retain talent. Employees want to see salary increases that correspond with market trajectories and company seniority. This can be quantified by analyzing market offerings and competitors’ salaries. Leadership should meet 1-to-1 to plan out career paths with their employees, which will also give them a better idea of their expected compensation trajectory.
Job titles are extremely important to retain talent. They can be used to reward seniority and to highlight the scope of responsibilities for a role. Setting the right job titles requires educating HR staff on different roles. Data analyst, data engineer, data architect and data scientist may sound similar, but each has distinct duties that come with different levels of experience and seniority.
One last thing hiring managers need to be aware of is the difference between the academic and engineering mindsets. Data science teams might consist of software engineers who are used to shipping products, and academic researchers who are comfortable with continuous fine-tuning. They might get frustrated with each others’ approach, but you still need both on a team. The best solution is to place each “mindset” in the right role. Let academia do the proper process of research, and have engineers in roles that focus on testing, deploying and monitoring models.
How can Vectice help?
Vectice captures the most valuable assets of AI/ML projects and stores them in one central place. Datasets, code, notebooks, models and runs are memorialized, annotated, and become searchable and reusable. This makes it easier for executives to find results themselves, so data scientists can focus on solving a core set of data problems. Vectice also helps to preserve successful outcomes of ML projects, which avoids transitional losses when employees leave.
One of the best ways to retain employees is to develop their skill set internally.
Read our next article to find out best practices for training AI/ML talent.