New opportunity

Team Lead of Biometrics Automation & AI Architecture

Quanticate · Bengaluru, India

About this role

We are looking for an exceptional leader to drive the next generation of biometrics innovation as Team Lead of Biometrics Automation & AI Architecture (also referred to as Director of Biometrics Automation & AI Architecture). In this role, you will transform traditional statistical programming into a modern, AI-powered ecosystem across clinical trials and real-world evidence programmes, leading the design and delivery of secure, intelligent solutions that streamline workflows and uphold data integrity.

Responsibilities

Develop and lead the organisation’s AI strategy for statistical programming, including deployment of secure enterprise AI platforms such as cloud-based LLM environments.
Build automated, metadata-driven pipelines that convert statistical analysis plans and shell specifications into production-ready code.
Establish governance frameworks for AI use, including SOPs, validation procedures, and audit controls aligned with GxP requirements.
Design safeguards within AI-driven processes to appropriately handle missing or incomplete clinical data without introducing bias.
Ensure strict data protection standards so that sensitive patient-level data and PHI are never exposed to AI systems.
Drive adoption of AI tools by training and mentoring statistical programmers and data scientists in structured prompting and automated validation techniques.
Collaborate cross-functionally with biostatistics, QA, and engineering teams to maintain regulatory compliance and technical excellence.

Required qualifications

Comprehensive understanding of clinical data standards, reporting practices, and regulatory frameworks.
Familiarity with AI technologies including large language models and retrieval-based systems.
Awareness of governance and validation requirements in regulated environments.
At least 8 years of relevant experience in biometrics or statistical programming within CRO, pharmaceutical, or healthcare research settings.
Practical experience with CDISC implementation and clinical deliverables.
Proven involvement in automation or transformation initiatives.
Experience operating within global, cross-functional teams.
Strong programming capability in R, Python, and SAS.

Benefits

Competitive salary
Flexible working hours
Holidays including Annual Leave, Sick Leave, Casual Leave and Bank holidays
Medical Insurance for self and immediate family
Gratuity
Accidental Coverage
Learning development opportunities such as mentoring, coaching, e-learning, and job shadowing

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

RPythonSASLLMsRAGCDISCGxP

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Enterprise AI Strategy and Secure Platform Deployment
Drive the organization's AI strategy for statistical programming by deploying secure enterprise AI platforms, such as cloud-based LLM environments, ensuring modern AI integration across clinical trials.
Automated Pipeline Engineering
Build automated, metadata-driven pipelines using R, Python, and SAS that seamlessly convert statistical analysis plans and shell specifications into production-ready code.
Regulatory Compliance and AI Governance Frameworks
Establish robust governance frameworks, SOPs, validation procedures, and audit controls aligned with GxP requirements and data protection standards to safeguard PHI.
Cross-Functional Mentorship and Tool Adoption
Train and mentor statistical programmers and data scientists in structured prompting, automated validation, and new AI workflows while collaborating across biostatistics, QA, and engineering teams.

Likely challenges to think through

Balancing AI Innovation with Strict GxP Compliance
The difficulty lies in introducing cutting-edge LLM and multi-agent systems into a heavily regulated clinical environment without compromising audit controls or data integrity. Prepare by reviewing standard GxP validation methodologies for software and defining clear audit trails for automated code generation.
Handling Incomplete Clinical Data Without Bias
Designing AI-driven processes that safely handle missing or incomplete clinical data introduces a risk of algorithmic bias. Prepare by establishing rigorous validation benchmarks and statistical guardrails specifically tailored to clinical trial datasets.
Driving Cultural Adoption Among Traditional Programmers
Transitioning traditional statistical programmers accustomed to legacy workflows (such as SAS) toward AI-powered automation can face resistance. Prepare by developing structured training programs and demonstrating early productivity wins through pilot metadata-driven pipelines.

Practice your answers

Technical practice

Designing Secure Enterprise LLM Environments for Clinical Data

What to coverDiscuss how you would design and deploy a secure enterprise AI platform (such as a cloud-based LLM environment) to support statistical programming while ensuring patient-level data and PHI are never exposed. Candidates should address data anonymization, network isolation, secure API gateways, and encryption standards.

Metadata-Driven Pipelines for Statistical Analysis Plans

What to coverExplain how you would build automated, metadata-driven pipelines that convert statistical analysis plans (SAPs) and shell specifications into production-ready code using languages like R, Python, or SAS. Focus on template design, validation checks, and maintaining traceability from shells to final deliverables.

Maintaining GxP Compliance and Validation Frameworks

What to coverWhat steps are involved in establishing governance frameworks, standard operating procedures (SOPs), and validation procedures for AI-driven statistical processes under GxP requirements? Candidates should discuss audit controls, change management, and software validation lifecycles.

Handling Missing or Incomplete Clinical Data Without Bias

What to coverHow would you design safeguards within automated or AI-driven processes to appropriately handle missing or incomplete clinical data without introducing bias? Discuss imputation techniques, statistical validation benchmarks, and risk mitigation strategies.

CDISC Implementation in Automated Clinical Deliverables

What to coverHow do you integrate CDISC standards (such as SDTM and ADaM) into automated metadata-driven clinical programming pipelines? Address data standardization, conformance rules, and automated quality control checks.

Evaluating Retrieval-Augmented Generation (RAG) in Regulated Settings

What to coverHow would you implement and evaluate a Retrieval-Augmented Generation (RAG) system for querying internal clinical trial guidelines and reporting practices? Focus on grounding, hallucination reduction, and validation procedures for retrieved outputs.

Mentoring and Training Teams on Structured Prompting

What to coverWhat is your approach to driving the adoption of AI tools by training and mentoring statistical programmers and data scientists in structured prompting and automated validation techniques? Discuss change management, hands-on workshops, and measuring adoption success.

Cross-Functional Collaboration for Regulatory and Technical Excellence

What to coverHow would you collaborate cross-functional with biostatistics, quality assurance (QA), and engineering teams to maintain regulatory compliance and technical excellence during an AI transformation initiative? Address communication channels, shared accountability, and compliance checkpoints.

Behavioural practice

Leading a Major Technology Transformation Initiative

What to coverDescribe a situation where you led a significant automation or technology transformation initiative in a regulated environment. In your response, use the situation-action-result format to highlight how you managed stakeholder expectations, addressed resistance, and delivered measurable outcomes.

Resolving Compliance and Innovation Trade-offs

What to coverTell me about a time when a proposed technical innovation or AI deployment clashed with strict regulatory or compliance requirements. Explain the situation, the specific trade-off you faced, the actions you took to align with governance standards, and the final result.

Cross-Functional Leadership Across Global Teams

What to coverGive an example of how you successfully operated within a global, cross-functional team to deliver a complex project. Describe the communication challenges, the leadership actions you took to keep teams aligned, and the impact on project timelines and quality.

Mentoring Team Members Through Technical Reskilling

What to coverDescribe an experience where you had to train and mentor team members transitioning to new technical tools or workflows (such as moving from legacy programming to modern automated platforms). Explain your mentoring strategy, how you handled learning curves, and the resulting team proficiency.

Sharpen your knowledge

Topics to prepare

GxP Governance and AI Validation in Clinical Trials
Review regulatory frameworks, SOPs, and audit controls for validating AI systems in regulated pharmaceutical environments. Exercise: Draft a sample validation SOP for an LLM-powered pipeline. Relevance: Directly addresses requirements for maintaining GxP compliance and audit controls.
Clinical Data Standards and CDISC Implementation
Revise clinical data standards, reporting practices, and CDISC implementation methodologies. Exercise: Map standard statistical analysis plans to CDISC-compliant data structures. Relevance: Essential for building metadata-driven pipelines and ensuring data integrity in clinical deliverables.
Secure Enterprise AI and Data Protection Frameworks
Study secure cloud-based LLM architectures and data privacy standards to prevent PHI exposure. Exercise: Outline an architecture design that isolates patient-level data from third-party AI endpoints. Relevance: Matches the requirement to ensure strict data protection and safe handling of sensitive patient data.

Skills to revise

R, Python, and SAS Programming for Automation
Practice writing and refactoring scripts in R, Python, and SAS to convert statistical analysis plans into production code. Exercise: Build a small prototype script in Python that automates shell specification conversion. Relevance: Aligns directly with the core programming capabilities required for the role.

Your preparation checklist

Prepare an Automation Architecture Portfolio Walkthrough
Compile examples of past automation or digital transformation initiatives in biometrics or statistical programming. Completion means having a structured presentation ready that details how you designed metadata-driven pipelines, utilized R, Python, or SAS, and streamlined clinical trial deliverables.
Draft a GxP and AI Governance Framework Proposal
Write a sample governance framework document outlining standard operating procedures (SOPs), validation procedures, and audit controls for deploying cloud-based LLM environments in regulated healthcare settings. Completion means having a concrete artifact demonstrating adherence to GxP standards.
Develop a Data Privacy and PHI Safeguard Strategy
Document a technical strategy for preventing patient-level data and Protected Health Information (PHI) exposure when interacting with AI systems. Completion means having clear architecture notes and anonymization safeguards ready for discussion.
Build a Prototype Metadata-Driven Pipeline Script
Create a working code prototype in Python or R that demonstrates converting statistical analysis plans or shell specifications into production-ready code. Completion means having a functional demonstration repository or code snippet to showcase technical execution.
Design a Training and Mentorship Curriculum Outline
Outline a training program aimed at upskilling statistical programmers and data scientists in structured prompting and automated validation techniques. Completion means having a modular curriculum plan ready to present to leadership.
Prepare Targeted Questions for the Employer
Formulate thoughtful questions regarding Quanticate's current biometrics infrastructure, existing cloud-based LLM platforms, and cross-functional collaboration between biostatistics, QA, and engineering teams. Completion means having a concise list of 4-5 high-value questions ready for interview discussions.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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