Senior Director, AI and Data Science - Drug Discovery

2w2 weeks ago

Lexeo Therapeutics

New York City, US · Full-time · $275,000 – $375,000

About this role

Lexeo is at an inflection point where AI and advanced analytics can materially accelerate decision-making across discovery, development, and operational execution. This Senior Director will set direction and deliver applied AI/ML solutions across internal workflows and externally facing outputs, ranging from R&D insights to partner-ready analyses. The role partners closely with scientific teams and external vendors to solve real problems.

The position is hands-on and outcome-driven, building, validating, and operationalizing models using real-world biopharma data. Focus on raising the signal-to-noise ratio in small or unstructured datasets, including synthetic control arm approaches where appropriate. Deliver solutions for scientific decision support, operational forecasting, and partner-ready analyses like regulatory dossiers and briefing books.

Collaborate across R&D, CMC, Clinical, Safety, and IT/Security to implement scalable data pipelines and AI-enabled workflows. Bridge scientific teams and data/engineering, ensuring solutions are scientifically credible and operationally adoptable. Contribute to initiatives like CMC AI automation, MaxisAI clinical database, and AI ingestion into Dataverse/Fabric.

Define and execute Lexeo’s applied AI/ML roadmap, prioritizing use cases that improve speed, quality, and decision confidence. Establish best practices for model lifecycle management in regulated workflows. Lead advanced analytics and predictive modeling tailored to biopharma constraints.

Requirements

Hands-on experience building, validating, and operationalizing AI/ML models using real-world biopharma data
Expertise in raising signal-to-noise ratio in small or unstructured datasets
Proficiency with ML models including XGBoost, Random Forest, SVMs, and advanced predictive approaches
Knowledge of techniques like regularization, Bayesian methods, hierarchical modeling, and synthetic control arms
Ability to translate scientific questions from drug discovery into analytical hypotheses
Experience partnering with bench scientists and cross-functional teams in R&D environments
Familiarity with data pipelines, platform integration, and AI workflow automation in biopharma
Skills in bias/variance trade-offs, interpretability, and model monitoring for regulated workflows

Responsibilities

Define and execute applied AI/ML roadmap across discovery and development, prioritizing use cases that improve speed, quality, and decision confidence
Deliver internal-only solutions like scientific decision support and operational forecasting, plus external-facing outputs like partner-ready analyses
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 context
Build and oversee predictive analytics using real-world data with robust evaluation and performance monitoring
Apply techniques to amplify signal-to-noise in small datasets such as regularization, Bayesian methods, and uncertainty quantification
Guide strategy for synthetic control arms ensuring methodological rigor and transparency
Translate drug discovery questions into testable hypotheses and partner with bench scientists to design data capture