IQVIA is a Global CRO with like 80-90k employees. They do two things really well. Running clinical trials (Phases I-IV) and vacuuming up healthcare data (claims, EMR, sales, etc., https://www.iqvia.com/insights/the-iqvia-institute/available...)
107 karma · joined December 15, 2015
Certilytics provides sophisticated predictive analytics solutions to major healthcare organizations by integrating financial, clinical, and behavioral insights. Our team represents a dynamic infusion of multidiscipline, which includes actuarial, data, and behavioral scientists, IT engineers, software developers, nurse clinicians, and experts in public health and the health insurance industry. Certilytics has extensive experience working with a diverse set of customers, including large self-insured employers, health plans, pharmacy benefit managers, government programs, care management companies, and health systems. These relationships with various data providers and customers allow for rapid data ingestion, validation, and enrichment, as well as streamlined delivery of analytic dashboards, outputs, and visualizations to our customers. Our unique approach allows for developing the most accurate financial, clinical and behavioral models in the industry.
Why Certilytics! Access to one of the most extensive clinical datasets in the industry that includes medical claims, pharmacy claims, and laboratory data. Impactful work. We're big enough to have the freedom to take on interesting projects but small enough that your work is always important and highly visible within the organization. Remote friendly. The Certilytics team is distributed throughout the US and has regular in-person working sessions for all of those little things that are hard to accomplish over Teams.
See our open jobs: https://jobs.silkroad.com/CarewiseHealth/Certilytics
Certilytics provides sophisticated predictive analytics solutions to major healthcare organizations by integrating financial, clinical, and behavioral insights. Our team represents a dynamic infusion of multidiscipline, which includes actuarial, data, and behavioral scientists, IT engineers, software developers, nurse clinicians, and experts in public health and the health insurance industry. Certilytics has extensive experience working with a diverse set of customers, including large self-insured employers, health plans, pharmacy benefit managers, government programs, care management companies, and health systems. These relationships with various data providers and customers allow for rapid data ingestion, validation, and enrichment, as well as streamlined delivery of analytic dashboards, outputs, and visualizations to our customers. Our unique approach allows for developing the most accurate financial, clinical and behavioral models in the industry.
Why Certilytics!
Access to one of the most extensive clinical datasets in the industry that includes medical claims, pharmacy claims, and laboratory data.
Impactful work. We're big enough to have the freedom to take on interesting projects but small enough that your work is always important and highly visible within the organization.
Remote friendly. The Certilytics data science team is distributed throughout the US and has regular in-person working sessions for all of those little things that are hard to accomplish over Teams.
Tech Stack: Python, TensorFlow, Kubernetes, AWS, Spark, Scala
See our open jobs: https://www.certilytics.com/contact-us/#careers
Do you enjoy reading the latest machine learning research on Arxiv? Do you challenge yourself to reverse engineer interesting papers? Do you seek to apply existing algorithms to new domains and develop creative and novel solutions to difficult problems? If so, come join our team at Certilytics!
Certilytics, Inc. provides sophisticated predictive analytics solutions to major healthcare organizations by integrating financial, clinical, and behavioral insights.
As a machine learning research engineer, you'll be responsible for designing and running experiments to bring the latest deep learning advances from the literature to our products. As part of the data science team, you will be responsible for building models for clinical and financial risk prediction, performing original research, and contributing to a proprietary machine learning library. The ideal candidate will have a strong background in natural language processing and familiarity with the inner workings of RNN’s and transformer networks (Join a flexible, energetic team in bringing the best of deep learning to healthcare.
Apply here: https://jobs.silkroad.com/CarewiseHealth/Certilytics/jobs/20...
It'll be interesting to see when specialized ML focused silicon will become readily available. Right now I find ML libraries that are able to run on blended architectures (any combination of CPU and GPU's) much more exciting/impactful than TPU's. The ability to deploy on just about any cluster a customer may have available is huge.
Also, 200k patients is actually kind of small. Granted this dataset is far more granular/robust than what you’d typically find in commercially available healthcare datasets, but to give you some frame of reference, the healthcare datasets I work with contain > 20 million individuals (again, with orders of magnitude fewer features).
Also check out this article on updates to R 3.4. R tends to be fast enough for most work (I use it regularly on one-off analysis or things that won't ever make it farther than ad-hoc reporting/findings but can't imagine using it in production systems). The listed changes should go a long way towards making R just fast(er) enough for dealing with larger datasets (doesn't help with datasets larger than memory though). For large datasets all the momentum seems to be moving towards Spark (sparklyr is RStudio's SparkR integration. Very much a beta but getting better by the day). On the Python front Dask is awesome for out of memory computation that has no equivalent in R.