A fast-growing Toronto-based Healthtech Startup are looking to add x2 Data engineers after recent funding. The role will join a small team who are rapidly expanding, if you want to get into a company early this will be a great time to join.
The client are on a mission to understand and structure the world’s medical data and are backed by some big players including Google Ventures.
As a Data Engineer you will:
- Develop data infrastructure to ingest, sanitize and normalize a broad range of medical data, such as electronics health records, journals, established medical ontologies, crowd-sourced labelling and other human inputs.
- Build performant and expressive interfaces to the data
- Build infrastructure to help us not only scale up data ingest, but large-scale cloud-based machine learning
- Experience building data pipelines from disparate sources
- Hands-on experience building and scaling up compute clusters
- Excitement about learning how to build and support machine learning pipelines that scale not just computationally, but in ways that are flexible, iterative, and geared for collaboration.
- A solid understanding of databases and large-scale data processing frameworks like Hadoop or Spark. You’ve not only worked with a variety of technologies, but know how to pick the right tool for the job.
- A unique combination of creative and analytic skills capable of designing a system capable of pulling together, training, and testing dozens of data sources under a unified ontology.
Bonus points if:
Developing systems to do or support machine learning, including experience working with NLP toolkits like Stanford CoreNLP, OpenNLP, and/or Python’s NLTK.
- Expertise with wrangling healthcare data and/or HIPAA.
- Experience with managing large-scale data labelling and acquisition, through tools such as through Amazon Turk or DeepDive.
To find out more please get in touch on email@example.com to find out more