The recruitment of talented data science professionals is a tough task for companies. A survey on Artificial Intelligence (AI) Adoption by O’Reilly in 2021 found that the topmost challenge faced is – “lack of skilled people or difficulty hiring required roles.”
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Top 5 Skills Needed to Make AI Work
Building AI solutions do not just require data scientists. A diverse set of functions has to be performed to have data in place – collect, curate, and store good-quality data. Further, these are the 5 must-have data science skills to design, build, and adopt AI–
- Domain expertise: Selecting the right business problems and execution approach
- Machine learning (ML): Identifying data insights and building AI models
- Software Engineering: Packaging models into a software application
- Information Design: Designing the workflow and providing model insights
- Managerial Expertise: Managing project uncertainties and ensuring adoption
Four Interesting Ways to Build Talent In-House
Look Beyond the IT Team
Stop the under-utilization of your staff and discover the hidden gems across your organization. This can be done by analyzing and documenting the skillsets of all employees. Classify them into four broad categories: expert, functioning, novice, and desired stretch assignment – making AI project resource allocation a lot easier.
Use Public Content to Design a Data Science Curriculum
Upskilling your in-house teams is a challenge. The online training portals and MOOCs (massive open online courses) may not serve your organization’s specific needs. Wendy Zhang, director of data governance and strategy at Sallie Mae says, “You must design your own curriculum by curating content from multiple online sources.” Create lesson plans based on employees’ backgrounds, roles, and future needs using informative free online resources. Leverage puzzles, games, and group activities to help apply learnings.
Bridge Technical Skills with Domain Expertise
A good blend of domain and technical expertise helps create the perfect AI solution. Upskilling in just one of these is insufficient. It’s essential to have a business orientation in technical training and provide real-world applications. Bridging the AI awareness gap leads to better quality ideas and project implementation.
On-the-Job Practical Learning and Training
It’s crucial that your teams do experiments, learn from mistakes, and develop future-ready skills on the job. Pair the beginners with more experienced employees (mentors) and set up a clear expectation for each. Second, define the tasks for the beginners where the mentors can support them.
Balance Your Teams’ Skills
Methodical training helps upskilling, but it must be balanced with soft skills, creativity, and communication lessons. This makes your team ready to build data science solutions that can transform your business.
Read the full article originally published on The Enterprisers Project by Gramener’s Co-founder and Chief Decision Scientist, Ganes Kesari.
