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

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 repeatable approach for small datasets and high-noise signals; documented modeling standards and review practices.  

• Strong partner engagement model: vendors/partners used strategically, with internal capability building and durable outcomes. 

• Direct experience in drug discovery, translational research, and/or R&D decision support (target ID/validation, MoA, biomarker strategy, preclinical data integration). 

• Experience with small data strategies, causality-aware thinking, and synthetic control arms or closely related methodologies.  

• Experience operating in regulated/quality-sensitive environments and building documentation practices that scale (particularly relevant where validation and traceability are required)  

• Familiarity with enterprise data platforms and modern analytics stacks (lakehouse/warehouse patterns, feature stores, MLOps, model monitoring).