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

Senior Director AI and Data Science Drug Discovery and R&D Enablement New York, New York G&A Full time Hybrid apply for this job

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. It requires a leader who can build, validate, and operationalize models using real-world biopharma data, raise the signal-to-noise ratio in small or unstructured datasets, and include synthetic control arm approaches where appropriate.

Key Responsibilities

• AI/ML Strategy & Delivery

• Define and execute Lexeos 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, and 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

• 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, and 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 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 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).

Compensation $255,000 - $302,000 a year Compensation is dependent on qualifications and experience.

About Lexeo Lexeo Therapeutics is a clinical-stage genetic medicine company headquartered in New York City, pioneering cardiac genetic medicine candidates to treat the root causes of inherited cardiovascular diseases. Our lead program LX2006 targets cardiomyopathy associated with Friedreichs Ataxia and anchors a broader pipeline addressing genetically defined conditions such as hypertrophic and arrhythmogenic cardiomyopathies. Backed by a strong financial foundation, Lexeo is positioned to translate groundbreaking science into durable clinical impact.

Our work culture is a hybrid model with 2 days/week in the New York City office and 3 days working from home.

Lexeo Therapeutics is an EEO employer committed to an exciting, diverse, and enriching work environment.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

apply for this job