Senior Research Scientist: Insilico Prediction Platform Lead

Role Summary

Senior Research Scientist: Insilico Prediction Platform Lead. In Silico Structural & Property Prediction Platform Leader driving strategy, development, and integration of automation, AI, and ML–powered prediction models to accelerate early-stage drug discovery across Elanco’s research portfolio. Shape the platform vision, partner with scientific and technical teams, and enable computational approaches that support both small and large molecule innovation. Build scalable, data-driven, and automated research capabilities within pharmaceutical R&D.

Responsibilities

• Develop and execute the technical strategy for Elanco‚Äôs structural and property prediction platform in alignment with key scientific and therapeutic priorities.
• Evaluate, select, and implement advanced AI/ML models‚Äîincluding generative, diffusion-based, deep learning, and graph-based approaches‚Äîto support small and large molecule discovery.
• Lead the integration of new computational and automation capabilities into existing discovery workflows and research pipelines.
• Establish standards and best practices for model ingestion, benchmarking, validation, deployment, reproducibility, and scientific data integrity.
• Build scalable workflows and automated processes that streamline predictive modeling and improve scientific throughput.
• Continuously evolve the platform by incorporating stakeholder feedback, monitoring performance indicators, and tracking scientific and technological advancements.

Qualifications

• Education: Bachelor‚Äôs, Master‚Äôs, or PhD in molecular biology, biotechnology, bioengineering, bioinformatics, cheminformatics, computational biology, data science, or a related scientific discipline.
• Experience: 8+ years in computational drug discovery, including at least 5 years leading AI/ML or automation-focused projects or platforms within pharmaceutical or biotech R&D.
• Demonstrated success developing, applying, and deploying advanced computational and automated workflows that accelerate drug discovery, with meaningful impact in large molecule programs.
• Ability to translate complex computational concepts into clear, actionable insights for multidisciplinary scientific and business partners.

(Scientific domain-first backgrounds are preferred; technical candidates without strong scientific grounding will not be prioritized.)

Preferred Qualifications

• Expertise developing, benchmarking, automating, and deploying state-of-the-art AI/ML models, including deep learning, generative models, diffusion models, GNNs, and transformer-based architectures.
• Strong scientific programming experience (Python, R, or C++), including building, optimizing, and productionizing cheminformatics and bioinformatics workflows using tools such as RDKit, TensorFlow, PyTorch, Benchling (or similar), AlphaFold, Protein LLMs, Schr√∂dinger, Chai, and Boltz; experience with Git-based CI/CD and reproducibility frameworks.
• Deep understanding of the drug discovery pipeline, including ADME/Tox, PK/PD modeling, SAR/QSAR, data analysis, protein engineering, molecular modeling, molecular dynamics/simulation, and structure-based design. Experience in animal health is a plus.
• Proven leadership and communication skills with a track record of influencing interdisciplinary scientific and technical teams and presenting complex modeling concepts to diverse audiences.
• Hands-on experience building scalable or cloud-based computational and automation platforms supporting virtual screening, high-throughput modeling, large-scale data analysis, and ML model training.

Education

• Bachelor‚Äôs, Master‚Äôs, or PhD in molecular biology, biotechnology, bioengineering, bioinformatics, cheminformatics, computational biology, data science, or a related field.

Additional Requirements

• None beyond those stated in Qualifications/Preferred Qualifications.