🔍 Jason Beckwith, managing director of biopharma recruitment specialist Evolution Search Partners, mentions a shortage of data scientists who can extract valuable insights from raw data.
⚙️ The growing use of machine learning in the industry has increased demand for expertise in support vector machine (SVM) technology.
💡 Solutions to this skills gap include changing the perception of talent as intangible intellectual assets and utilizing specialists in talent acquisition.
🔧 Technology suppliers could also play a role in providing the necessary skills through advanced training programs.
👉 This article highlights the need for the biopharma industry to address the data science skills gap to harness the full potential of digital transformation.
Introduction:
Biopharma’s digital transition has led to a skills gap in data science, particularly in areas such as integration, analysis, statistical modeling, and machine learning. The use of machine learning has increased, resulting in a higher demand for staff with expertise in technologies like support vector machine (SVM) and understanding the infrastructure for data exchange in bioprocessing systems. The industry also struggles to hire programmers proficient in programming languages such as Python for digital manufacturing technologies.
- The skills gap in data science is evident in areas such as integration, analysis, statistical modeling, and machine learning.
- The use of machine learning has increased, leading to a higher demand for expertise in support vector machine (SVM) technology.
- The industry struggles to attract staff who understand the infrastructure for data exchange in bioprocessing systems, particularly with technologies like AWS and Microsoft Azure.
- Hiring programmers proficient in languages like Python for digital manufacturing technologies is challenging due to compensation parity with other sectors.
- To address the skills shortfall, companies should treat talent as intangible intellectual assets and consider using specialists to attract talent. Technology suppliers can also play a role in ensuring the availability of required skills.
Conclusion:
The data science skills gap in digital biopharma poses challenges in areas such as integration, analysis, and machine learning. To overcome this gap, companies should prioritize talent management, consider using specialists for recruitment, and collaborate with technology suppliers to ensure access to the required skills.
