Senior Director, AI and Data Science

3w3 weeks ago

Lexeo Therapeutics

New York City, US · Full-time · $300,000 – $400,000

About this role

Lexeo is at an inflection point where AI and advanced analytics accelerate decision-making across discovery, development, and operations. This Senior Director sets direction and delivers applied AI/ML solutions for internal workflows and external outputs like partner analyses. The role is hands-on, building and operationalizing models with real-world biopharma data to boost signal-to-noise in small datasets.

Day-to-day involves defining AI/ML roadmaps prioritizing use cases for speed and decision confidence. Deliver internal tools like scientific decision support and external materials such as regulatory dossiers. Establish model lifecycle practices including validation, monitoring, and retraining for regulated workflows.

Lead predictive modeling with techniques like XGBoost, Random Forest, and SVMs, addressing data constraints and interpretability. Apply small data methods such as Bayesian modeling, regularization, and synthetic controls. Partner closely with bench scientists to frame hypotheses and design data capture.

Bridge scientific teams and data/engineering for credible, adoptable solutions. Collaborate cross-functionally with R&D, CMC, Clinical, and IT on pipelines and AI initiatives like CMC automation and Dataverse integration. Guide external vendors for outcome-driven problem-solving in drug discovery.

Requirements

Hands-on experience building, validating, and operationalizing ML models using real-world biopharma data
Expertise in advanced ML models such as XGBoost, Random Forest, SVMs, considering interpretability and deployment
Proficiency in small data techniques like regularization, Bayesian methods, hierarchical modeling, and uncertainty quantification
Knowledge of synthetic control arms and comparable approaches for methodological rigor in decision-making
Ability to translate scientific questions in drug discovery into analytical hypotheses
Experience partnering with bench scientists and cross-functional teams in R&D environments
Familiarity with enterprise platforms and initiatives like Dataverse/Fabric, Benchling AI API

Responsibilities

Define and execute applied AI/ML roadmap across discovery and development, prioritizing use cases that improve speed, quality, and decision confidence
Deliver internal solutions like scientific decision support and external-facing outputs such as partner-ready analyses and validated dashboards
Establish best practices for model lifecycle management including validation, documentation, monitoring, and retraining
Lead development and selection of ML approaches like XGBoost, Random Forest, SVMs based on data constraints and deployment needs
Build predictive analytics using real-world biopharma data with robust evaluation and performance monitoring
Apply techniques to amplify signal in small datasets including regularization, Bayesian methods, and synthetic control arms
Translate drug discovery questions into testable hypotheses and partner with scientists on data capture for modeling
Partner cross-functionally with R&D, CMC, Clinical, and IT on scalable data pipelines and AI workflow automation