Challenges for Statistical Programmers in the Coming Years

The field of statistical programming in the clinical trial industry is undergoing rapid transformation. As automation, artificial intelligence, and open-source tools become more prevalent, statistical programmers must adapt to remain competitive and relevant.

1. Automation and Artificial Intelligence (AI)

Automation and AI are revolutionizing statistical programming, streamlining processes, and reducing manual effort. While this increases efficiency, it also presents a challenge—junior programmers may have fewer opportunities to build foundational skills. Many complex algorithms are now encapsulated in macros, making it unnecessary for programmers to develop code from scratch. As a result, newer programmers may lack expertise in core procedures such as PROC REPORT or PROC SGPLOT.

Programmers feel AI will take a big chunk of their everyday work and will not offer anything in exchange. This may lead to programmers using AI to create outputs, limiting their exposure to intellectually engaging work.

At the same time, there is a lot of hype about AI taking over programming roles in the next 5 to 10 years, reducing the number of programmers needed for clinical studies. While this might sound concerning, many believe we are still far from AI fully replacing human programmers. AI can generate code, but it lacks human intuition and problem-solving abilities. Instead of fearing AI, programmers should experiment with these tools and integrate them into their workflow to enhance efficiency.

A good programmer will always find a way to remain valuable by understanding complex requirements, writing efficient code, and contributing beyond what AI can achieve.

2. Economic Challenges and Job Market Shifts

Due to high interest rates, many companies are reducing costs, opting for cheaper alternatives, and outsourcing programming roles. Large pharmaceutical companies have expanded their presence in South Asia, significantly reducing programming resources in the US. A similar trend is visible in Contract Research Organizations (CROs).

Many experienced programmers have been let go in the past six months and are still searching for jobs. In over 20 years in the industry, we have never seen such concerns about job security. This situation is particularly critical for those on work visas, as they must secure a new position within 60 days to avoid losing their status and returning to their home countries.

The advice? Stay calm, focus on excelling in your current role, and remember that market downturns are often cyclical. The situation may improve over time.

3. The Shift from SAS to Open-Source Tools (R and Python)

Historically, SAS has dominated statistical programming in the pharmaceutical industry. However, many companies are now transitioning to open-source tools like R and Python to reduce licensing costs and enhance flexibility. This shift requires programmers to learn new languages and frameworks.

While the transition is gradual due to the existing SAS infrastructure, programmers who do not adapt may find themselves at a disadvantage. Learning R and Python early will provide a competitive edge, ensuring career stability in an industry that is slowly but inevitably moving toward open-source solutions. As a recruiter, I can confirm that the frequency of R-related interview questions has risen exponentially—I now ask every candidate.

4. Increasing Workload and Pressure for Faster Submissions (Do more with less)

The demand for faster clinical trial submissions has intensified, driven by competition among pharmaceutical companies. AI and machine learning have significantly reduced R&D timelines, but the burden often falls on statistical programmers to accelerate data processing and analysis. This has led to increased stress, burnout, and reduced job satisfaction.

One major concern is the lack of adequate support systems. Many companies expect quicker turnarounds without providing the necessary tools or process optimizations. To combat this, programmers should seek companies that invest in efficient workflows and realistic project timelines. Organizations must also recognize the need for better resource allocation and workload management to prevent burnout.

5. Narrow Specialization and the Risk of Obsolescence

Many statistical programmers specialize in specific therapeutic areas, such as oncology, which has been a major focus in recent years. However, therapeutic areas evolve based on industry needs. If breakthroughs in oncology slow down and new areas, such as antibiotic resistance, gain prominence, programmers who have overly specialized may struggle to transition.

To remain adaptable, programmers should develop expertise in multiple therapeutic areas and stay informed about emerging trends in drug development. This will help them remain in demand regardless of industry shifts.

6. Challenges in Metadata Management and Regulatory Compliance

In the race to meet submission deadlines, metadata management sometimes takes a backseat. Proper documentation, including define.xml files, is crucial for regulatory agencies like the FDA and PMDA. However, with increasing time constraints, metadata generation often becomes inconsistent.

The solution lies in automating metadata management and ensuring consistency across all phases of programming. The industry should invest in standardizing metadata processes to reduce manual errors and compliance risks.

7. Declining Motivation and Career Growth Challenges

With increasing automation, repetitive tasks, and workplace distractions, maintaining motivation can be challenging. Over time, programmers may feel stagnant in their roles, especially if they are not learning new skills or facing new challenges.

Remote work remains the norm in the industry, but some pharmaceutical companies are pushing for a return to the office. If this trend continues, it could negatively impact programmers who prefer working independently. Many programmers are introverts who do not thrive in open-space environments.

To combat this, setting personal and professional development goals is crucial. Regularly assessing one’s growth—asking, “Am I a better programmer this year than last year?”—can clarify areas for improvement. Additionally, exploring interests outside programming, such as sports, family time, or creative hobbies, can help maintain a healthy work-life balance and prevent burnout.

The Future of Statistical Programming

As AI continues to evolve and industry standards change, many statistical programmers wonder what the field will look like in the next 5–10 years. Will the demand for programmers decrease? What skills will be most valuable?

While the exact future remains uncertain, one thing is clear: adaptability is key. Programmers who continuously learn, embrace new technologies, and understand the principles behind automation will always be in demand. The industry is moving towards greater collaboration, knowledge sharing, and the use of standardized tools, which presents opportunities for those willing to evolve.

A good statistical programmer must focus on fundamental skills: understanding requirements, efficient programming, writing clear dataset specifications, effective communication, problem-solving, teamwork, and mentoring abilities. Strengthening these skills will help programmers navigate future industry challenges.

?