In demand AI and Cheminformatics Talent
As the global Artificial Intelligence (AI) drug discovery market continues to grow in monetary value at a rapid pace, with a projected growth at CAGR of 39.9% until 2026, as stated by Global News Wire 2022, there has unsurprisingly been an increase in companies attempting to recruit AI. In tandem, Cheminformatics tools have become increasingly in demand in drug discovery. Companies are beginning to acknowledge the vast impact chemistry software can have on not only the action of identifying leads in drug discovery, but the time that is also involved according to Mordor Intelligence, 2022. That said, the supply of qualified candidates in the AI and Cheminformatics (ChemAI) drug discovery space is yet to match demand.
So how can biotechnology and pharmaceutical companies ensure they are able to attract, hire and importantly retain this in demand talent? We reveal what talent acquisition strategies organizations can apply to meet their business goals.
Within ChemAI, the majority of the roles can be performed remotely, so the talent pool could double or even triple in size if companies offered the option for remote working. This would also form part of their retention strategy, as they would also avoid losing candidates to their competitors that do provide this flexibility. Even offering flexibility which includes only having to come on-site 1 week a month, garners greater attention from prospective candidates.
In the post-Covid world, the competitiveness of a job offer is now decided on an additional factor: remote working opportunity. Many candidates will factor flexibility into their decision making, preferring an offer that gives them the opportunity to work remotely for at least part of the week. Candidates have been known to reject a job offer with a higher salary, preferring to accept an offer with greater remote options.
Many companies have begun granting greater flexibility in remote working in order to secure the best talent. However, this typically occurs after a long search for a local candidate, before realizing they may be chasing their tail. On average, once organizations loosen their on-site demands, they see a greater number of applications, interviews, and eventually hires.
Companies could also compromise on their expectations by considering candidates in other industries (medical device, chemical or pharmaceutical manufacturing etc.) with transferable skillsets. Instead of trying to find a “unicorn” with expertise in both fields, they could widen their target talent pool by interviewing AI/Machine Learning (ML) experts with an emerging or foundational level of chemistry knowledge, or vice versa.
Too often, organizations are looking for a niche set of skills in a niche industry domain. If they cannot flex on the skillset required, they could flex on the industry. AI/ML is used in multiple industries and there is no shortage of capable candidates. Drug discovery companies could turn to chemical manufacturing, pharmaceutical manufacturing, agricultural, technology and digital health to find the right fit. Many of these skills are transferable and should be seen as such.
Recent examples of the success in this approach includes a drug discovery company seeking a Machine Learning Scientist with Protein Engineering capabilities. Originally, this organization were focused on candidates already within drug discovery, or at least healthcare broadly. They shifted their focus to include candidates in agricultural companies and chemical manufacturing companies where Machine Learning Scientists were handling plant and animal protein. Though different to the role they would perform within the drug discovery organization, the transition could be smooth, considering their experience. As a result, they were able to hire an experienced Machine Learning Scientist by casting their net wider.
Companies have discovered unexpected success in hiring less experienced candidates and providing them with the technical leadership and training. Current PhD students are utilizing state of the art technology which businesses can greatly profit from if they widen their pool. Instead of demanding 1+ years’ experience in industry, organizations could consider graduating candidates, whose minds are fresh with the knowledge they have been applying for the last few years.
In addition, organizations have seen great success with hiring PhD students for internships and looking at Masters or Bachelors level candidates for their full-time hires. Whilst compromising on the level of experience, companies will find these candidates are less costly and can be taught ormentored over time. For the most part, companies have expressed their surprise at the technical skills of their less experienced hires This mentorship by senior leadership also garners long term results, as it allows for more internal promotions.
Below is a summary of one of the most in demand jobs that is simultaneously the most difficult for biotechnology and pharmaceutical companies to fill and research.
Hybrid scientists: Machine Learning and Computational Chemistry (Senior-Principal)
2022 saw a continuous trend of US based clients looking to kill two birds with one stone by hiring a AI/ML technical expert with the scientific understanding of a computational chemist/cheminformatician, within drug discovery. Characteristically, the job requirements include an expertise in Deep Learning, typically generative modelling, as well as experience with Cheminformatics tools and an understanding of Organic or Medicinal Chemistry. Put simply these scientists are near impossible to recruit, for a number of reasons.
Firstly, these positions are often on-site or hybrid as a minimum, meaning qualified candidates have to be located near company headquarters or relocate, narrowing the talent pool immediately. Secondly, deep learning expertise is not entirely rare in itself, albeit rarer than a traditional machine learning scientist. However, identifying and successfully recruiting a deep learning expert with expertise in computational chemistry/cheminformatics is a difficult feat. Candidates with a PhD in Computational Chemistry or Organic/Medicinal Chemistry, do not typically embark on career paths which expose them to deep learning model development, as this is often the responsibility of pure AI scientists. Whilst they may understand the methodology, they do not often apply it like an Computer Science graduate will.
Lastly, candidates that meet the requirements above are, naturally, highly valued and well paid in their current role, as their rare skillset is acknowledged. With the booming presence of so many promising biotechs in the US, companies need a competitive edge to stand above the rest. As such, compensation has soared above market average. Base salaries for candidates with 1-2 years postdoctoral experience vary between $160-180k with a 12-15% bonus, whilst candidates with 3+ years postdoctoral experience varies between $190-220k with a 15-20% bonus.
Talent acquisition strategies
Overall, these are the talent acquisition strategies that can be utilised to ensure a quick and smooth hiring process. As the AI and Cheminformatics market continues to be competitive, organizations can adopt greater flexibility in their search for the “right fit”, to avoid spending months chasing their tail. Looking for transferable skills and upskilling are tips that can help with identifying the right talent, whilst remote working is a tactic to ensure success in securing this talent and retaining valued staff. Moving forward in 2023, organizations should attempt these strategies to lessen the cost and time it takes to fill their niche vacancies.