Senior Director, AI and Data Science

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. Solutions range from R&D insights to partner-ready analyses, partnering with scientific teams and external vendors.

The role is hands-on and outcome-driven, building, validating, and operationalizing models using real-world biopharma data to raise signal-to-noise in small or unstructured datasets. Deliver internal solutions like scientific decision support and operational forecasting, plus external-facing validated dashboards and decision materials. Establish best practices for model lifecycle management, validation, documentation, monitoring, and retraining.

Lead advanced analytics and predictive modeling with ML approaches like XGBoost, Random Forest, SVMs, considering data constraints and interpretability. Apply techniques for small data excellence, including regularization, Bayesian methods, hierarchical modeling, and uncertainty quantification. Guide synthetic control arms ensuring methodological rigor and transparency.

Translate drug discovery questions into testable hypotheses, partnering with bench scientists for data capture. Serve as bridge between scientific teams and data/engineering for credible solutions. Partner across R&D, CMC, Clinical, Safety, IT to implement scalable pipelines and AI workflows.

Contribute leadership to AI initiatives like CMC automation, MaxisAI clinical database, Dataverse/Fabric integration, and Benchling AI API. Hands-on focus solves real problems in drug discovery and R&D enablement. Outcome-driven impact accelerates biopharma decisions.

Requirements

Hands-on experience building, validating, operationalizing AI/ML models with real-world biopharma data
Expertise in ML for small or unstructured datasets using regularization, augmentation, multimodal integration, feature engineering
Proficiency with advanced models like XGBoost, Random Forest, SVMs considering interpretability and bias/variance trade-offs
Knowledge of synthetic control arms and comparable approaches for decision-making in R&D
Ability to translate drug discovery and translational questions into testable analytical hypotheses
Experience partnering with bench scientists to design data capture for strong modeling
Track record bridging scientific teams with data/engineering for adoptable solutions
Familiarity with AI integrations like Dataverse/Fabric, Benchling AI API, CMC automation

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 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 problem framing 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 including Bayesian methods, hierarchical modeling, and uncertainty quantification
Guide strategy for synthetic control arms ensuring rigor, transparency, and fit-for-purpose use
Partner with cross-functional stakeholders to implement scalable data pipelines and AI-enabled workflows