Advisor R&D

Elanco•Indianapolis, IN

2d•Onsite

About The Position

As the Senior Machine Learning & Computer Vision Scientist, you will accelerate Elanco’s R&D portfolio by turning proprietary scientific data into predictive models that guide drug discovery and development decisions. You will partner with discovery and development scientists to build machine learning and computer‑vision models on molecular, in vitro, ADMET, and imaging data to support target identification, molecular optimization, digital biomarkers, and endpoint prediction for animal health.

Requirements

PhD in a quantitative field (e.g., Data Science, Engineering, Physics, Mathematics, Statistics, Bioinformatics, Computational Chemistry), or a Master’s degree with substantial hands‑on ML/CV experience.
7+ years of industry experience building and deploying ML and computer‑vision models for scientific problems in biomedical, pharmaceutical, or related domains, with demonstrated impact and close collaboration with wet‑lab or clinical teams.
Strong Python skills and proficiency with modern ML/CV libraries (e.g., PyTorch, TensorFlow, scikit‑learn, OpenCV), solid understanding of statistics and experimental design, and proven ability to work and communicate effectively in cross‑functional, matrixed environments.

Nice To Haves

Experience in animal health, pharmaceutical, or biotech R&D, with a record of impactful ML/CV applications (e.g., chemoinformatics, bioinformatics, digital biomarkers) shown through projects, publications, or patents.
Hands‑on expertise with methods such as neural networks and generative models, plus practical experience operationalizing models using Databricks and MLOps tools in production‑like or scaled environments.
Demonstrated success leading cross‑functional teams, mentoring junior scientists, and influencing scientific and technical decisions in complex, global organizations.

Responsibilities

Lead ML/CV work for key R&D programs, partnering with scientists to turn target identification, molecular optimization, in vitro analysis, ADMET, and biomarker questions into modeling problems with clear success criteria.
Design, build, and validate advanced ML and computer‑vision models—including CNNs, transformers, graph neural networks, and generative approaches—on multi‑modal data (molecular, assay, imaging, behavioral) to deliver reliable predictions and classifications.
Shape data collection, annotation, and synthetic data usage by working with experimental teams on capture protocols, labeling standards, and quality checks; evaluate synthetic data and augmentation to strengthen sparse datasets while managing bias and risk.
Operationalize models in R&D workflows by integrating them with tools used by scientists—including building and deploying models in Databricks—and influencing standards for reproducibility, documentation, monitoring, and model governance.
Provide technical leadership and mentorship by guiding junior scientists, leading cross‑functional modeling initiatives, and clearly communicating technical concepts and results to diverse stakeholders.