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-enabledworkflows.
• 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.

Required Skills and Qualifications

• 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.

Preferred Skills and Qualifications

• 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).