Understanding the need to hire a richly diverse team rather than looking for one hire who can do everything. Building your team from the ground up to lay the foundations for a successful future. The importance of clear and strong direction to communicate insight.Speaking
Discussing the debate between hiring new talent and upskilling your current workforce. The benefits of upskilling scientists who already understand the culture and organization that they’ll be working in. Broader business benefits of training current workers – achieving quicker results, understanding where the existing challenges lie, maximizing existing relationships.Speaking
Academia is one of the few areas with large numbers of AI practitioners with a strong background in research Integrating academic researchers into an AI team Applying learnings and techniques from research to solve business problemsSpeaking
Strategies for identifying the best talent with the most relevant projects. Structuring team between internal and external facing problems. Balancing between short and long term goals.Speaking
Using freelance talent to identify the scale of transformation using AI The benefits of shorter term investment to provide strong foundations Providing freelancers with clear direction to make best use of their unique skillset
AI talent isn’t always a lack of technical skill but of communication Highlighting the need for specialists with communication or data visualization specialism Understanding the need for skills outside of traditional AI talentSpeaking
Encouraging engineers to think past the short term by creating a career path. Creating an organization which people want to work for by showing a clear plan for the future. Improving employee engagement through active collaboration with the broader industry. Providing scientists with autonomy to pursue personal as well as business interests.Speaking
Evaluate the prospect of creating identifying one quick win to act as a case study to secure greater investment Using a small project as a testing ground for data scientists to test their AI skills in practice Building outwards from and adding talent when needed, rather than creating the infrastructure at great costSpeaking
Lack of talent doesn’t always mean lack of available talent but an unwillingness to pay the market rate. The need to pay what others will in order to achieve top level talent – and getting board buy-in. Making your business attractive to compete with early adoptersSpeaking
Without laying the foundations and creating the necessary infrastructure – an effective data pipeline, an culture receptive to AI, obvious processes to modernize – much of the work of AI scientist could be done by other employees. Hiring the right people for the right roles, and having a clear understanding of those roles. Having direction… Read more.
Skills needed vary depending on whether your organization builds the solution in house or uses an out-the-box solution. Different skills and levels of experience are needed depending on the approach. The need to hire for how the role will develop, not just generically for certain skills.