Join HOPPR as a Data Scientist and play a pivotal role in shaping the future of multimodal AI in medicine.
Collaborate with researchers, engineers, and clinicians to enhance data infrastructure and develop impactful solutions with vast amounts of unstructured datasets like radiology scans, patient reports, and electronic health records (EHRs).
You’ll tackle complex challenges and drive innovations that transform patient care.
Design and develop robust pipelines using advanced methods with large language models (LLMs) to extract features and label data from unstructured datasets.
Create and implement rigorous evaluation metrics to assess feature extraction processes, ensuring continuous improvement aligned with clinical and product goals.
Enhance and maintain scalable, reproducible data science infrastructure to support agile development and secure operations across partitioned client environments.
Design and implement MLOps practices to streamline, scale, and automate machine learning workflows.
Manipulate, analyze, and manage large-scale datasets using Python, SQL, and other tools.
Work closely with engineers, clinicians, and product teams to ensure data solutions are aligned with user needs and drive meaningful outcomes.
Thrive in a dynamic and rewarding environment that emphasizes excellence, autonomy, and impact.

Master’s or PhD in Computer Science, Engineering, Data Science, or a related field.
1+ years of professional data science experience, with a proven ability to train, evaluate, and deploy machine learning models, including large language models.
Proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow), as well as experience with data manipulation tools like SQL, pandas, or NumPy.
Familiarity with ML Ops practices and deploying models into production pipelines (preferred).
Knowledge of healthcare data, such as radiology images or EHRs, is a plus.
Strong ownership mindset, entrepreneurial spirit, and product-focused approach to solving impactful problems.

Data Scientist

Key skills