Machine Learning & Computer Vision Scientist – R&D (Junior/Associate)

Elanco•Indianapolis, IN

5d•Hybrid

About The Position

As the Machine Learning & Computer Vision Scientist (Junior/Associate), you will help drive Elanco’s R&D innovation by implementing and refining ML and computer‑vision models that support faster, better‑informed decisions. You will work closely with senior scientists and R&D stakeholders to turn proprietary molecular, in vitro, imaging, and digital endpoint data into predictive and classification models for target identification, molecular optimization, ADMET, and digital biomarkers in animal health.

Requirements

Master’s degree in a quantitative field (e.g., Data Science, Engineering, Mathematics, Physics, Statistics, Bioinformatics)
0–3 years applying ML and/or computer vision to real datasets through academic projects, internships, industry roles, or open‑source work, with evidence of hands‑on model development and evaluation.
Proficiency in Python and familiarity with ML/CV libraries such as scikit‑learn, PyTorch or TensorFlow, and OpenCV
Understanding of core ML concepts and basic deep learning
A collaborative, clear‑communicating working style.

Nice To Haves

Exposure to scientific datasets (e.g., high‑content imaging, histopathology, microscopy, behavioral video, assay data) and completed projects, theses, or publications showing applied ML/CV skills with scientific or healthcare relevance.
Familiarity with training deep learning models on GPUs, use of Git and reproducible workflows, and exposure to data platforms or cloud environments such as Databricks, Azure, or AWS.
Demonstrated ability to learn quickly, iterate on models and analyses, and contribute to reusable code, tools, or documentation that improve team efficiency and quality.

Responsibilities

Implement and refine ML/CV models under guidance, developing, training, and tuning models on R&D datasets (molecular, in vitro, imaging, behavioral) in collaboration with senior scientists to align with scientific objectives.
Prepare and manage datasets for modeling by cleaning, transforming, and merging data from multiple scientific sources, running exploratory analyses, and contributing to feature engineering and clear dataset documentation.
Support data collection, annotation, and synthetic data work by helping improve capture and labeling workflows for images, video, and assay data, assisting with annotation guidelines, and evaluating synthetic data and augmentation to improve sparse datasets and model robustness.
Assist with model evaluation and reporting by contributing to train/validation/test design, applying appropriate metrics and error analyses, and documenting methods, assumptions, limitations, and results for review and reuse.
Collaborate and grow ML/CV expertise by joining cross‑functional project meetings, sharing learnings through short demos or presentations, and actively building knowledge in drug discovery, development, and ML/CV techniques.