Senior Director, AI and Data Science - Drug Discovery

2w2 weeks ago

Lexeo Therapeutics

New York City, US · Full-time · $280,000 – $400,000

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. The role is hands-on and outcome-driven, focusing on real-world biopharma data.

Build, validate, and operationalize models to raise the signal-to-noise ratio in small or unstructured datasets, including synthetic control arm approaches. Partner closely with scientific teams and external vendors to solve real problems. Deliver internal decision support and partner-ready analyses like regulatory dossiers and briefing books.

Lead as a bridge between scientific teams and data/engineering, ensuring solutions are scientifically credible and operationally adoptable. Translate drug discovery questions into testable hypotheses and design data capture for strong modeling. Collaborate across R&D for platform integration and cross-functional enablement.

Define 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. Drive 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
Proficiency in ML approaches including XGBoost, Random Forest, SVMs, and advanced models suited to biopharma constraints
Expertise in techniques for small or unstructured datasets such as regularization, Bayesian methods, hierarchical modeling, and uncertainty quantification
Knowledge of synthetic control arms and comparable methods for decision-making in R&D
Ability to translate drug discovery and translational questions into analytical hypotheses
Experience partnering with scientific teams to design data capture and ensure model credibility
Skills in model lifecycle management for regulated workflows including validation, monitoring, and retraining
Strong background in predictive analytics, feature engineering, and multimodal data integration in pharma

Responsibilities

Define and execute Lexeo’s applied AI/ML roadmap across discovery and development, prioritizing use cases that improve speed, quality, and decision confidence
Deliver solutions that are internal-only (e.g., scientific decision support, operational forecasting) and external-facing (e.g., partner-ready analyses, validated dashboards)
Establish best practices for model lifecycle management (validation, documentation, monitoring, retraining), especially where outputs influence scientific decisions or regulated workflows
Lead development and selection of appropriate ML approaches (e.g., XGBoost, Random Forest, SVMs) based on problem framing, data constraints, interpretability, and deployment context
Build and oversee predictive analytics using real-world data, including robust evaluation design, bias/variance trade-offs, and performance monitoring
Apply techniques to amplify signal-to-noise in smaller datasets (e.g., regularization, Bayesian methods, hierarchical modeling, augmentation, multimodal integration)
Guide strategy for synthetic control arms and comparable approaches, ensuring methodological rigor, transparency, and fit-for-purpose use
Translate drug discovery and translational questions into testable analytical hypotheses; partner with bench scientists to design data capture