Job Description
- Experience level: Mid
- Tech stack used: Python, PyTorch, AWS
- Primary skills we consider: Python, PyTorch, Tensorflow
- Secondary skills we consider: AWS, Sklearn
- Compensation: Competitive
- Employment type: Permanent & Full time
- Visa sponsorship: Available
About you
You are an ambitious and capable machine learning scientist, with direct experience developing probabilistic graphical models (e.g. hierarchical Hidden Markov Models), preferably in a biomedical context. You have a strong research background, either in an academic or industrial lab. You are conversant with the relevant branches of mathematical statistics. You care about impact and want to do work that matters.
List of main duties and responsibilities:
- Applying probabilistic graphical models to measure biomarkers
- Conducting research and authoring academic papers
- Contributing to our research strategy
- Keeping up to date with relevant research
Requirements:
- Strong understanding of probabilistic graphical models (eg. HMMs)
- Demonstrable experience applying probabilistic graphical models to real world problems
- PhD in relevant quantitative subject (statistics, mathematical engineering, etc)
- Good communication and data visualization skills
- Experience with core Python data science packages (numpy, pandas, sklearn)
Desirable:
- Publications at top ML conferences/journals
- PhD in probabilistic graphical models
- Previous experience in the biomedical sector
- Experience with at least one deep learning framework (PyTorch or TensorFlow)
- Familiarity with git, bash and coding best practices.
- Familiarity with cloud infrastructure (AWS EC2, S3, Batch)