Hiring Strategy to Win the Data Science and AI Talent Acquisition Race
With the phenomena of The Great Resignation subsiding, the focus is back on AI talent acquisition in the key data science industries. In the last 5 years, the demand for AI and machine learning engineers and data scientists sky-rocketed to Himalayan levels. With new technologies and AI applications finding their way into mainstream markets, it is a plausible guess on how data science companies are planning their AI talent acquisition strategy.
In the latest report from Prolego, winning the AI talent acquisition race is a multi-dimensional strategy that requires an equal participation from every department, particularly the Human Resources team, the first touch points for most aspiring data scientists looking to work for you. By hiring the right AI leaders, by providing the right tools and resources, and by creating a collaborative data-driven culture, organizations can create an ideal hiring and retention environment for data scientists.
As the demand for AI and machine learning jobs get wider and complex, it is easy to ascertain that the skill gap in IT-specific is something that every business leader would acknowledge. As AI becomes a mainstay in non-IT functions such as Marketing, Sales, Finance, HR and business intelligence, and non-IT industries such as Healthcare, Digital Marketing, Manufacturing and Education, the fight for every talented data science professional available in the job market is going to become more intense. And, the way companies advertise AI and Machine Learning job opening online, data scientists and AI engineers often feel trapped in the classical scheme of things where “non-AI business managers” are calling the shots in hiring without understanding the difference between AI, ML and data science roles and functions, as applied to a live practical project.
According to Prolego’s ML Workforce report, it is the job of the hiring managers and business leaders to create an apt environment for data scientists where data scientists are not doing traditional IT operations, cleaning data or creating dashboards. In short, give data scientists an environment where they do work that they love doing– DATA SCIENCE.
If you are an AI company or a data science project organization, finding the best data science talent is your “secret ingredient” to building an effective and culture-driven workplace where data science can thrive to deliver great results– something that AI and Machine Learning is embraced to do.
This is what Prolego’s ML Workforce Report found out.
Quick Overview on what companies should do to win the machine learning and AI talent acquisition race in 2022.
Hire AI and Data Scientists when you need them
Most organizations are hiring data scientists or AI engineers to do basic data management tasks. There is a growing call from the business leaders and AI professionals to state how AI and ML roles advertised seem very different from what is actually done. And, data scientists hate this ambiguity.
Before you plan to hire a data scientist, please ensure there is a data science work for them. Once you hire them, please don’t interfere in their data science work. They know data science better than any other job title can ever describe.
Give autonomy to data scientists
Data scientists prefer to work in a stress-free environment where the autonomy lies with them. Data scientist role may be the sexiest job title in the modern industrial era, but it is far from being easy. It is complex, involves a play of hard skills, and big data intelligence.
Recommended HR Technology News: New Features of SutiHR Optimize Candidate Review Process
Encourage Self-paced Learning
The AI/ML world constantly changes. It is incredibly tough for a data scientist to catch a missed bus if not enough time and resources are invested in learning the new skills required to stay relevant in the AI ML world. Clearly, the new machine learning and data science techniques require additional learning and skill matching, even if it means taking time off the work to set the bar higher. Allow the data scientists to typically spend more hours on AI and data science specific learning. Average could range between 8 and 10 hours per week.
This learning will help your company in solving the problems related to managing disparate data, breaking silos and attracting more AI ML professionals, because you invest in building a data empire where data scientists have autonomy to pace their learning, do their work and deliver results with best of world’s data infrastructure, people and their aspirations.
Hippocratic Oath, but for AI / ML
Ethical dimensions of AI/ML and data science have far-reaching effect on the modern society. In the data science, we are yet to see a solid push for ethics in AI. Brent Ferrier, Principal Data Scientist at Oshkosh Corporation said, “I think generally the data scientists I’ve worked with throughout my career do understand the responsibility that comes with being a decision-maker or at least supplying the insights to the decision-makers, and that kind of goes back to the immaturity of the field. There is no code of ethics or a regulation equivalent to the Hippocratic oath for [AI].”
Prolego recommends data scientists should be hired and integrated into your AI Ethics Program, with likely benefits visible in the form of more job satisfaction.
Focus on Becoming a Talent Magnet for Highly-skilled and Well-trained Data Scientists
AI leaders seldom complain about talent shortfall. They acknowledge the gaps and find solutions to fill that gap in AI/ ML roles with unmatched visionary activities. And, size and age of the business, it seems is inversely proportional to the job satisfaction levels among data scientists. For instance, Prolego’s ML career chart reveals data scientists are more likely to feel more satisfaction in a growth stage startup or an established technology compared to a FAANG/ big tech or legacy non-technology organization.
Conclusion:
AI leaders who understand the hiring trends should play a decisive role in bringing to front the essence of data culture, competitive compensation and enhanced communication. In the AI talent acquisition race, it’s important that you design a relevant AI ML job description, hire with leadership and talent development in mind, and then empower data scientists to do the work they love most– data science.