Research Data Scientist
Role details
Job location
Tech stack
Job description
The Senior Data Scientist will operate as part of an internal consultancy supporting Principal Investigators across diverse research domains. This role blends applied machine learning, data engineering, containerization, and large-scale compute orchestration. The work is project-based, research-driven, and highly collaborative, requiring a high level of technical breadth and autonomy to help researchers translate scientific questions into operational workflows., * Partner with Principal Investigators to understand scientific goals, data availability, and technical constraints.
- Translate research questions into actionable machine learning and artificial intelligence workflows.
- Build prototypes, proofs of concept, and experimental pipelines to test solutions.
- Summarize and visualize results for research teams.
- Containerize workloads using Docker and deploy them at scale using Kubernetes.
- Use workflow orchestration tools such as Kubeflow, MLflow, or Airflow.
- Work with large, complex datasets across distributed systems and address big data performance challenges.
- Develop, evaluate, and refine models across diverse datasets and domains, including computer vision, natural language processing, and deep learning.
Requirements
Education: A Master's degree is required, preferably in Computer Science, Data Science, Healthcare Informatics, or a related field.
Experience: Hands-on experience with Kubernetes, including deployments, jobs, metrics, and scaling, is required. Experience with containerization, scheduling, and workflow orchestration tools is also necessary. Healthcare or clinical research experience is preferred.
Technical Skills: Strong Python programming skills are required. Experience with machine learning and deep learning frameworks such as PyTorch or TensorFlow is needed. Familiarity with alternative data structures like graph databases, NoSQL databases, object storage, and vector databases is expected. Experience with medical imaging is a significant advantage., * Experience supporting academic or clinical research environments.
- Experience with long-running distributed compute jobs.
- Familiarity with Large Language Models or on-premise LLM environments.
- Experience with scientific computing environments.