Associate Director, Biostatistics & AI

What You’ll Do

The Associate Director, Biostatistics & AI is a senior individual contributor and emerging people leader responsible for the delivery of high-quality statistical analyses and for designing, building, and embedding AI/ML interfaces into the day-to-day workflows of the biometrics function. This role sits at the intersection of pharmaceutical statistical science and applied AI engineering — and is the primary internal resource for setting up, validating, and governing AI tooling in a GxP-compliant environment.

A successful candidate will bring either deep pharmaceutical biometrics expertise with growing AI/ML skills, or strong AI/ML engineering experience with meaningful exposure to clinical or life sciences data. In either case, they will have the drive and aptitude to rapidly bridge both domains. They will work across cross-functional teams including Clinical Development, Regulatory Affairs, Medical Affairs, and Pharmacovigilance, and will manage CRO statisticians and programmers on assigned programs.

Responsibilities

Pharmaceutical Biostatistics

•    Apply appropriate statistical methodologies supporting medical insights, value demonstration, safety monitoring, and lifecycle management

•    Contribute to statistical design, analysis, and reporting for Phase II–IV clinical trials, including adaptive designs, Bayesian methods, and estimands under ICH E9(R1)

•    Contribute to NDA, BLA, MAA, and other global regulatory submissions as lead or co-statistician; support responses to health authority queries

•    Stay current with FDA, EMA, and ICH guidance on statistical methodology, and real-world evidence (RWE)

AI Interface Setup & Implementation

•    Design and implement AI/ML interface environments connecting biometric platforms (SAS, R, Python) with enterprise LLM APIs, RAG pipelines, and clinical data repositories

•    Build, test, and iterate on AI-powered tools for SAP drafting, TFL automation, anomaly detection, protocol deviation identification, and regulatory document support

•    Establish and maintain MLOps pipelines for model development, validation, deployment, and monitoring within a GxP-compliant, audit-ready framework

•    Implement AI governance standards including explainability (SHAP/XAI), bias assessment, and 21 CFR Part 11 / EU Annex 11 compliant documentation

•    Evaluate and deploy AI-powered automation for CDISC mapping, data cleaning, and submission package preparation

•    Partner with IT and third-party vendors to integrate AI tools with EDC, CTMS, RTSM, and pharmacovigilance systems

Cross-Functional Collaboration & CRO Oversight

•    Translate statistical and AI concepts into clear, actionable insights for medical, regulatory, and commercial stakeholders

•    Partner with Clinical Development, Regulatory Affairs, Medical Affairs, and Pharmacovigilance to align biometrics and AI deliverables with program timelines

•    Develop and maintain SOPs, work instructions, and best practices for biostatistics, AI tooling, and statistical programming

Where You’ll Work

This is a U.S.-based remote role. Quarterly visits to our [City, State] office, or as needed based on program requirements.

Who You Are

•    PhD or Master’s degree in Biostatistics, Statistics, Computer Science, Artificial Intelligence, Data Science, or a closely related quantitative field

•    We recognize that strong candidates may come from different professional paths. We welcome applications from either of the following profiles:

Track A — Biostats-First with Applied AI Skills

5+ years (PhD) or 7+ years (Master’s) of pharmaceutical biostatistics experience in drug development, biotech, or CRO settings

Expert-level proficiency in SAS and/or R; working knowledge of Python for data science or scripting

Demonstrated exposure to AI/ML tools — such as LLM APIs, prompt engineering, or automation scripting — with a strong interest in expanding these capabilities in a GxP environment

Deep knowledge of CDISC standards (SDTM, ADaM, Define-XML) and regulatory submission workflows; experience contributing to NDA/BLA/MAA preferred

Track B — AI/ML-First with Pharma/Biostats Exposure

3+ years of hands-on AI/ML engineering experience, including building and deploying LLM-powered tools, RAG pipelines (LangChain or equivalent), agentic workflows, and/or MLOps infrastructure

Strong Python proficiency for AI/ML development; working familiarity with SAS and/or R in a statistical or data science context

Meaningful exposure to clinical or life sciences data — e.g., working with clinical trial datasets, CDISC standards,