Senior Director, AI and Data Science (Drug Discovery and R&D Enablement)
Scorpion Therapeutics, New York, NY, United States
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 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).
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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.
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).
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