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

Role Summary

Lexeo is at an inflection point where AI and advanced analytics can materially accelerate decision‑making across discovery, development, and operational execution. This Sr. 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, while partnering closely with scientific teams and, when needed, external vendors/partners to solve real problems.

This role is intentionally hands‑on and outcome‑driven: a leader who can build, validate, and operationalize models using real‑world biopharma data to raise the signal‑to‑noise ratio in small or unstructured datasets (including synthetic control arm approaches where appropriate).

• 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).

• A prioritized AI/analytics roadmap tied to measurable R&D outcomes; clear ownership and delivery cadence.
• 2‑4 production‑grade analytics solutions adopted by teams (internal and/or external‑facing outputs as needed).
• A repeatable approach for small datasets and high‑noise signals;…