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). 

Key Responsibilities

AI/ML Strategy + Delivery  

• 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. 

Advanced Analytics + Predictive Modeling 

• 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. 

Small Data Excellence + Synthetic Controls  

• 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. 

Drug Discovery / Translational Partnership 

• 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. 

Cross-functional Enablement + Platform Integration 

• 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). 

External Partner/Vendor Leadership 

• Liaise with external partners to evaluate tools, define statements of work, and deliver solutions—while ensuring knowledge transfer and sustainable internal ownership.  

Operational Excellence 

• 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). 
What Success Looks like (First 6-12 Months)
• 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 repeata