What is the difference between a data scientist and a machine learning engineer in pharma?

Data scientists focus on analysis and modeling, while machine learning engineers build, implement, and maintain machine learning and AI systems that solve problems.

Does this chapter include country-specific insights?

Yes. It highlights four countries where pharma companies can source AI and data talent, along with workforce characteristics and hiring considerations.

What are the biggest risks when hiring AI and data science roles internationally?

Key risks include labor law compliance, termination requirements, working-time regulations, and data privacy obligations, especially when handling sensitive clinical data.

How did Safeguard Global develop the intelligence used to create this report?

This report is built on our unique combination of AI-driven workforce analytics from Safeguard Global’s Intelligent Workforce platform and Actual Intelligence from in-country experts across 70+ countries. This approach allows pharmaceutical leaders to move beyond surface-level data and understand how real-world hiring conditions, regulations, and talent expectations vary by country.

Where can I access the full report?

You can access the complete Industry Intelligence Report here: Where to Source Pharma Talent in 2026: Top Countries for Hard-to-Fill Life Sciences Roles.

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Hiring Machine Learning Engineers and Data Scientists for the Pharmaceutical Industry

Chapter 1 from Safeguard Global’s Industry Intelligence report, Where to Source Pharma Talent in 2026: Top Countries for Hard-to-Fill Life Sciences Roles

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Hiring Machine Learning Engineers and Data Scientists

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Machine learning engineers and data scientists support critical pharmaceutical functions, from drug discovery and clinical trials to real-world evidence and supply chain optimization. As AI adoption accelerates, pharma companies face growing competition for professionals with both technical expertise and life sciences experience.

This downloadable chapter explores where pharmaceutical companies can find machine learning engineers and data scientists globally and what hiring leaders should consider when expanding these teams.

What you’ll learn in this free report chapter

Hiring Machine Learning Engineers and Data Scientists is an excerpt from Safeguard Global’s Top Countries for Hard-to-Fill Life Sciences Roles report. This chapter focuses specifically on AI-related and data science roles that are among the most difficult to source globally.

In the full PDF chapter, you’ll learn:

Which countries offer the strongest talent pools for pharma AI roles
How labor costs and competition differ across global markets
Where English proficiency and collaboration are strongest
What regulatory and compliance factors employers must evaluate
Where technical AI skills are abundant and where pharma domain expertise is harder to find

Why hiring AI talent is a critical challenge for pharma companies

Demand for AI and data science talent continues to outpace supply, especially within regulated life sciences environments. As the global pharmaceutical market grows from $1.646 trillion USD in 2024 to a projected $2.35 trillion USD by 2030 (Grand View Research), access to specialized expertise has become a key constraint. Increasingly, pharma leaders are looking beyond domestic markets to address talent shortages using global hiring strategies to align advanced technical skills with regulatory and operational requirements.

Where pharma companies are finding AI and data talent

This chapter highlights countries with strong pools of machine learning engineers and data scientists, based on talent availability, hiring conditions, and local workforce dynamics. Each market offers different advantages, from established AI ecosystems to more cost-effective hiring environments. Key considerations for evaluating these regions as part of long-term workforce planning are also included.

Download the chapter or explore the full report

Use the image above to download Chapter 1: Hiring Machine Learning Engineers and Data Scientists, an excerpt from Safeguard Global’s Industry Intelligence Report.

Published in November 2025, the full report — Where to Source Pharma Talent in 2026: Top Countries for Hard-to-Fill Life Sciences Roles — is a practical guide for global hiring in the life sciences sector. It brings together workforce data, in-country insights, and expert analysis to help organizations understand where specialized pharma talent is growing and what factors influence hiring decisions.

Access the full report here for a comprehensive view of global pharma talent sourcing and workforce planning.

Frequently Asked Questions (FAQs)

This report chapter is designed for decision-makers responsible for talent, growth, and innovation in life sciences, including pharmaceutical and biotech executives, talent acquisition and workforce planning leaders, HR and people operations teams, R&D and digital transformation leaders, and companies planning international expansion or AI initiatives.

If your organization is struggling to hire machine learning engineers or data scientists with pharma expertise, this chapter provides practical, actionable guidance.

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