Biometrics teams rely on programming languages to analyze clinical trial data, meet regulatory requirements, and manage large datasets efficiently. The right programming skills make a major difference, whether it’s ensuring compliance with FDA and EMA standards or building automation into clinical workflows.

For hiring managers, understanding which programming languages are essential in biometrics is key to attracting the right statistical programmers and biostatisticians. While SAS has long been the dominant language for regulatory submissions, R, Python, and automation-driven approaches are shifting hiring priorities.

This guide explores the leading statistical programming languages used in biometrics today, the roles that require them, and what hiring managers need to know when building a strong biostatistics and programming team.

Why Statistical Programming Languages Matter in Biometrics

Clinical trials generate vast amounts of data. Every dataset needs to be analyzed, validated, and formatted for regulatory submission. Statistical programming languages are the tools that make this possible, but no single language does everything.

Some programming languages are built for regulatory reporting, ensuring data meets FDA and EMA submission standards. Others are designed for exploratory analysis, automation, or machine learning applications in clinical research. The choice of programming language impacts how efficiently teams process data, how securely information is managed, and how easily results can be validated for compliance.

For biometrics hiring, this means no single skill set is enough. Companies need programmers who can work across multiple programming environments, balancing structured regulatory programming with more flexible, innovation-driven approaches.

Why Different Languages Are Used in Biometrics

• Regulatory compliance: Certain languages, such as SAS, are widely accepted by global health agencies and are required for submission-ready datasets.
• Data complexity: Biometrics teams deal with structured clinical trial data, real-world evidence, and machine-generated datasets. Different programming environments are needed to handle these effectively.
• Flexibility and scalability: Some languages are optimized for statistical modeling, while others integrate better with automation and AI-driven workflows.
• Team expertise and industry shifts: SAS remains dominant, but more teams are adopting R and Python for specific use cases, creating a shift in hiring priorities.

As clinical research becomes more data-intensive, hiring the right mix of statistical programmers, biostatisticians, and data scientists has never been more important. The next section explores the leading statistical programming languages used in biometrics today and how they impact hiring.

Leading Statistical Programming Languages in Biometrics

Biometrics teams in clinical research use programming languages to process trial data, support regulatory submissions, and manage complex datasets. The language chosen directly impacts data accuracy, reproducibility, and compliance with global regulatory standards.

SAS, R, and Python dominate statistical programming in biometrics, but each serves a distinct purpose. Some are essential for submission-ready datasets, while others drive exploratory analysis, automation, or AI-driven insights. Understanding when and why these languages are used is key for hiring managers looking to build high-performing biometrics teams.

SAS: The Regulatory Standard for Clinical Trials

SAS is the most established programming language in clinical trials and biometrics. It is trusted by pharmaceutical companies, contract research organizations, and regulatory bodies because of its ability to produce structured, validated datasets for submissions. SAS remains essential for any research team handling large-scale trials that require strict regulatory compliance.

How Common is SAS in Biometrics?: 

• SAS programming in clinical trials is still the most widely used because FDA and EMA submissions require data formatted in CDISC-compliant datasets such as SDTM and ADaM.
• Pharmaceutical companies and CROs continue to prioritize SAS expertise in biometrics recruitment because it remains the standard for regulatory reporting.

When and Where SAS is Used in Clinical Research

SAS is essential when clinical trial data must be structured for regulatory approval. It is the leading tool for submissions to the FDA, EMA, and PMDA, ensuring datasets follow compliance guidelines and reducing the risk of regulatory delays.

It is also the industry standard where structured reporting is required. 

SAS is the preferred tool for creating tables, figures, and listings (TFLs) that are essential in clinical trial documentation. In pharmacovigilance and safety monitoring, SAS is used to analyze adverse event reports and ensure accurate tracking of patient safety data.

Key Job Roles That Require SAS:

• SAS Programmer – Develops validated statistical programs for trial analysis.
• Biostatistician – Uses SAS for statistical modeling and regulatory reporting.
• Regulatory Submission Specialist – Prepares submission-ready datasets for FDA and EMA approvals.

R: A Top Choice for Statistical Analysis and Real-World Data

R is one of the most powerful statistical programming languages used in biometrics. Biostatisticians and data scientists favor it for its flexibility in advanced statistical modeling, real-world evidence analysis, and data visualization. Unlike SAS, which is built for regulatory submissions, R provides more control over statistical methods and greater flexibility for exploratory analysis.

How Common is R in Biometrics?:

• R is being used more frequently in clinical trials as its statistical capabilities and flexibility make it well-suited for complex data analysis and modeling.
• Regulatory agencies are increasingly open to R-based submissions, especially in real-world evidence studies, though SAS remains the dominant language for compliance-driven work.
• The demand for R programmers and biostatisticians with R expertise is growing as pharmaceutical companies look for specialists who can handle more complex statistical modeling.

When and Where R is Used in Biometrics:

R is most valuable when research teams need flexibility in data analysis. It is widely used in early-phase clinical trials, where exploratory data analysis helps researchers refine study design before large-scale testing begins.

It is also critical where research teams must work with less structured datasets. R processes electronic health records, patient registries, and post-market surveillance data in real-world evidence studies. Its data visualization capabilities also make it useful when statistical findings must be presented in a clear and interpretable format.

R is particularly valuable when trial methodologies require adaptive decision-making. In adaptive trials, R enables researchers to incorporate real-time data adjustments, improving the efficiency of trials that evolve based on participant responses.

Key Job Roles That Require R:

• Biostatistician – Uses R for statistical modeling and clinical trial design.
• R Programmer – Writes statistical programs for data analysis and visualization.
• Real-World Evidence Analyst – Works with healthcare datasets to extract regulatory and commercial insights.

Python: The Future of AI and Automation in Biometrics

Python is becoming a critical programming language in biometrics, particularly in areas where automation, AI, and machine learning are being integrated into clinical research. While SAS and R remain dominant for regulatory and statistical analysis, Python is playing a larger role in predictive analytics, real-world data integration, and process automation.

How Common is Python in Biometrics?

• Python has remained a dominant language in data science hiring, appearing in 57% of data scientist job postings in 2024, highlighting its continued demand in data-driven fields, including biometrics.
• Many biometrics teams now use Python alongside SAS and R, particularly in research areas that require machine learning and predictive modeling.
• Python is widely used in real-world data integration, allowing researchers to analyze complex datasets from electronic health records, wearables, and patient monitoring systems.

When and Where Python is Used in Biometrics

Python is most valuable when clinical trials involve automation and machine learning. It is widely used in data validation and transformation, streamlining data management processes that would otherwise require extensive manual work.

It is also a leading choice where predictive analytics can improve trial outcomes. 

In drug discovery and biomarker research, Python helps researchers analyze patient response data, improving trial design and patient recruitment strategies.

Python is becoming essential when biometrics teams need to integrate AI and machine learning applications. Natural language processing (NLP) tools allow Python to analyze unstructured clinical trial documents, extracting insights that can streamline trial workflows and reduce inefficiencies.

Key Job Roles That Require Python:

• Clinical Data Scientist – Uses Python for AI-driven biometrics research.
• Bioinformatics Specialist – Applies Python to genomic and proteomic data analysis.
• Automation Engineer – Develops machine learning tools to optimize clinical trial workflows.

Choosing the Right Programming Language for Biometrics

The choice of programming language in biometrics affects how clinical trial data is analyzed, validated, and prepared for regulatory approval. Companies are no longer hiring statistical programmers based on expertise in a single language. Instead, they seek talent who can work across SAS, R, and Python to meet regulatory standards while integrating advanced data analytics into their research.

For hiring managers, this means recruitment decisions must be based on more than technical skills. The right team will have a mix of regulatory knowledge, statistical expertise, and automation capabilities.

Shifting Hiring Priorities in Statistical Programming

Pharmaceutical companies and contract research organizations conducting late-stage clinical trials continue to hire SAS programmers with expertise in regulatory compliance and structured reporting. At the same time, the demand for biostatisticians skilled in R is increasing, particularly in real-world evidence studies and exploratory data analysis.

Many teams are also adding Python specialists to support automation and machine learning applications in clinical research. Regulatory agencies are gradually becoming more open to alternative programming approaches, meaning teams must be able to work across multiple statistical environments.

This shift is not about replacing SAS but about strengthening capabilities in:

• Data science to support more complex clinical trial methodologies
• AI and automation to improve trial efficiency and predictive modeling
• Real-time analytics to integrate electronic health records, wearable data, and other sources

The Demand for Hybrid Skills in Statistical Programming

Biometrics teams must be structured to support regulatory reporting, advanced statistical modeling, and automation. The strongest teams include programmers who can work across multiple programming environments rather than relying on a single language.

SAS and R: Balancing Compliance and Statistical Flexibility

Many companies prefer statistical programmers and biostatisticians who are proficient in both SAS and R. This allows them to manage regulatory submissions while also supporting more flexible statistical analysis.

• SAS remains essential for CDISC-compliant regulatory submissions
• R is widely used in adaptive trials and real-world evidence studies
• Biostatisticians and clinical programmers who can transition between both languages are in high demand

R and Python: The Expanding Role of AI and Real-World Data

The growing use of machine learning and real-world data in clinical trials has increased demand for programmers with experience in R and Python. This combination is particularly valuable in:

• Predictive modeling for patient response analysis and trial optimization
• Machine learning-driven risk assessment in clinical trials and regulatory reviews
• Real-world data analysis integrating electronic health records, wearable technology, and post-market surveillance data

SAS and Python: The Shift Toward Automated Regulatory Reporting

Regulatory reporting is becoming more automated as companies look for ways to improve efficiency. Many organizations now prioritize SAS programmers with Python expertise to:

• Develop automated validation workflows for regulatory submissions
• Improve data transformation and machine learning-driven quality control
• Support AI-driven regulatory processes, enhancing the speed and accuracy of trial reporting

What This Means for Biometrics Hiring

Biometrics programming skills are changing as clinical trials become more data-driven. SAS programmers, particularly those with R experience, are still widely used in regulatory teams, which allows for more flexibility in statistical modelling alongside compliance-driven reporting.

For roles involving real-world data and AI-driven analytics, R and Python are becoming more valuable. These languages support automation, predictive modelling, and machine learning, particularly in large-scale data analysis research.

In regulatory teams adopting automation, SAS programmers with Python skills are helping streamline data validation and submission reporting, improving efficiency in compliance workflows.

With automation and real-world data playing a greater role in clinical trials, demand is growing for professionals with expertise in SAS, R, and Python, ensuring teams have the right mix of regulatory and data science capabilities.

Final Thoughts: Future-Proofing Biometrics Teams 

As clinical research becomes more data-driven, biometrics teams need professionals who can work across regulatory programming, statistical modeling, and automation. SAS, R, and Python remain the key programming languages shaping hiring priorities, with demand growing for multi-skilled professionals who can support both compliance and data-driven insights.

Building a team with the right balance of technical expertise and regulatory understanding is essential for keeping pace with industry advancements. Employers who invest in programmers with hybrid skills will be best positioned to manage both traditional clinical trial requirements and emerging AI-driven approaches.

Finding the Right Statistical Programming Talent

Competition for biostatistics and statistical programming talent is growing. At Warman O’Brien, we specialise in connecting life sciences companies with top biometrics professionals. Whether you need SAS programmers for regulatory submissions, R experts for statistical modelling, or Python specialists for automation and AI-driven analytics, we can help you find the right talent to build a high-performing team.

Speak to our biometrics recruitment specialists today and discover how we can support your hiring strategy.