Proven Tips for Landing Top AI Talent
Written by AInBC's Administrative Manager and Athena Pathways Project Lead: Norma Sheane
Artificial intelligence is a new and emerging field and there are some unique characteristics to look for when hiring for AI roles. Here’s a list of things to keep in mind along with specific examples to help you find and secure top talent.
1. Define the role with specific terms
It’s important to be specific about the AI skillset you need.
For example, are you working with neural networks, and if so, do you need someone with experience with GANs? Until recently there were no degrees specifically in AI so there weren’t the obvious pathways into a career that more established disciplines have. Yes, many practitioners have studied machine learning in a Masters or PhD capacity, but you often encounter people skilled in this field who have come to it through a more generalized area of study like mathematics, computer science, data science, or physics.
If you need to hire someone who can speak French, stating that you need someone from Europe is not specific enough. You need to know what part of the AI universe your job candidates are coming from.
2. Find lateral thinkers
The skillset for AI positions is different than for typical software development roles.
In the case of developers, projects are typically well defined and structured, with clear milestones, start and endpoints, as well as little follow-up. AI projects often require exploratory and experimental work with unclear timelines. Plus, the nature of an AI system is that it will improve significantly once it gets exposed to first training data and then production data. It will constantly be improving and may require continual attention from the developer.
AI roles often require interaction and teamwork with a number of people in the organization because data may come from a number of different areas. This is also because the AI teams within a company are typically not large so practitioners need to handle many different parts of the project and the people in the company who interface with the system.
Role requirements should include variety and critical thinking because the person in this role may need to "wear a lot of hats" and interface with many different parts of the organization.
3. Set your expectations on salaries
Here are general salary ranges in Canadian dollars for some typical roles:
ML Researcher - $125,000-$350,000
Data Scientist - $55,000-$190,000
ML Engineer - $75,000-150,000
Data Engineer - $55,000-130,000
In more senior positions, there is a big difference between good and great in terms of how far someone will be able to take your company. Expect to pay competitive salaries for people who can easily pick up a job in global centres like San Francisco or Singapore.
4. Work with agencies who know the area
If you went to France not knowing the language, would you want a foreign tour company to show you around or a French one that’s connected to the local businesses and knows the language?
In AI, there are many places you can look to find different levels of talent but using a generic recruitment agency may not produce effective results as this area is highly specialized. Work with a recruiter with demonstrated knowledge and a network in this area - for example AInBC’s AI role placement service: https://www.ainbc.ai/placements
Job boards are typically effective however they can generate hundreds of applicants for a single role creating the challenge of filtering through and finding the small percentage of qualified candidates. Some VP’s say that 80% of the applications they’re receiving are not even relevant. For senior roles, the applicant pool may not produce even one individual who will be able to do the job at a high level.
5. Ensure ethical alignment
If you are building something that operates in an ethical gray zone - due to it being a rapidly developing area or otherwise - ensure you check the applicant's comfort level. For example, if you aim to make facial recognition technology, the person designing it will need to be comfortable with how your company will deploy it (i.e. for human health vs. for law enforcement).
6. For junior talent, ask around at universities
When looking for junior talent, contact universities as they can provide a number of opportunities to find talent. Professors can make recommendations of top students. You can sponsor hackathons and attend university hiring fairs and poster sessions. Also consider specialized talent programs that recruit students, programs like Insight Data Science.
7. Check for hard skills
AI sits within data science, and since data science is a broad discipline which encompasses many skillsets, any number of people can call themselves a data scientist. But that doesn’t mean they know enough to do the job you have.
Applicants should be able to demonstrate that they can work with the technologies needed to commercialize the product. Academics may not have needed to code production-ready systems and may have relied on others to do coding work.
Applicants should be given a scenario to solve, to demonstrate they can be creative and think outside the box. They should also be able to demonstrate that they stay up to date on the latest developments in AI since the pace of change in the industry is so rapid.
These tips can help you navigate the new realm of hiring AI talent. It also helps if you know someone who’s an AI expert. Reach out to them for a virtual coffee and pick their brain. It will be well worth it to get your data straight and your machines set on “smart”!
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