Hybrid Senior Director, AI and Data Science – Drug Discovery, R&D Enablement

at Lexeo Therapeutics

• Job title: Senior Director, AI and Data Science – Drug Discovery, R&D Enablement
• Job type: Full Time
• Experience level: Senior
• Salary: $255,000 - $302,000 per year
• Degree requirement: Postgraduate Degree
• Location requirements: Hybrid, New York City, United States

About the role

• Senior Director leading AI and Data Science initiatives at Lexeo to enhance drug discovery and R&D enablement. Focused on developing applied AI/ML strategies and solutions in a cross-functional setting.

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 those that are generated internally but external-facing (e.g., partner-ready analyses (regulatory dossiers, briefing books, protocols etc.), validated dashboards, and decision materials).
• 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, and other advanced models) based on problem framing, data constraints, interpretability needs, 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, careful feature engineering, uncertainty quantification).
• Guide strategy for synthetic control arms and comparable approaches (as appropriate), ensuring methodological rigor, transparency, and fit-for-purpose use in decision-making.
• Translate drug discovery and translational questions into testable analytical hypotheses; partner with bench scientists to design data capture that enables strong modeling.
• Serve as a bridge between scientific teams and data/engineering, ensuring solutions are scientifically credible and operationally adoptable.
• Partner with stakeholders across R&D, CMC, Clinical, Safety, and IT/Security to implement scalable data pipelines and AI-enabled workflows.
• Contribute leadership to current and emerging initiatives such as AI workflow automation/database buildouts and analytics agents that leverage enterprise platforms (examples already in motion include CMC AI automation, MaxisAI clinical database/AI efforts, and AI work to ingest historical data into Dataverse/Fabric for agent-based analysis; integration work such as a Benchling AI API initiative may also be in scope depending on priorities).
• Liaise with external partners to evaluate tools, define statements of work, and deliver solutions—while ensuring knowledge transfer and sustainable internal ownership.
• Improve internal processes through automation and analytics, focusing on measurable impact (cycle time, error reduction, throughput, decision latency).
• Establish practical governance for data quality, documentation, and fit-for-use standards aligned with the realities of biopharma environments (including where regulated practices apply).

Requirements

• Advanced degree in a quantitative or scientific discipline (PhD strongly preferred; MS with exceptional experience considered).
• 10+ years of relevant experience across applied data science/ML in life sciences/biopharma (or adjacent domain with direct drug discovery translation), including 5+ years leading teams and influencing senior stakeholders.
• Deep familiarity with advanced ML methods (including XGBoost, Random Forest, SVMs) and the judgment to select and justify the right tool for the job.
• Demonstrated experience building predictive models with real-world, imperfect datasets and delivering them into production or decision workflows.
• Proven ability to improve processes and operationalize analytics—moving beyond prototypes to adoption.
• Strong cross-functional communication: can partner with scientists, engineers, and executives; can explain model performance and limitations clearly.