Counterpoint to that is when a recruiter initiates contact they are often very coy until they have you on the phone, frequently not even sending a JD until after that first conversation (where this line of questioning usually arises). I get wanting to provide an engaging response but hard to do so until the recruiter has provided sufficient information.
Certilytics | Hiring Data Engineers, Product Managers, Analysts, Developers, and more | Remote: US Only | Full-time
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.
Certilytics | Hiring Data Scientists and Data Engineers | Remote: US Only | Full-time
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.
Certilytics | Louisville, KY | REMOTE (US Only) | Machine Learning Research Engineer
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.
A subscription to safaribooksonline.com ($399/year) is my favorite way to spend part of my learning budget. Having access to the entire catalog of O’Reilly (and it’s affiliates) books is awesome. Access to conference recordings from Strata is really nice too.
From my experiences (currently work with several Fortune 100 health insurers/benefits managers, and have previously worked for another large insurer, a major academic medical center, and a large pharma company), healthcare organizations tend to be rather cloud adverse (most of our contracts very explicitly forbid us from using any form of 3rd party cloud computing). So while I agree that much of the heavy lifting will shift to the cloud (or already has), I expect health analytics will continue to favor on-premises solutions (GPU’s still tend to be pretty rare compared to CPU based clusters but are slowly becoming more common).
That's fair. Google's recent paper on predicting patient deaths is another good example of this (logistic regression + good feature engineering performed just as well as their deep learning models, and the logistic regression has the added benefit of being significantly more interpretable and as a result, actionable).
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.
I've always viewed DeepMind as more of a skunk works program and less as a profit driven enterprise. DeepMind exists primarily to push the limits of what can be done when you put group of leading researchers together in a room, provide them with nearly limitless resources, and simply tell them to "go". I expect some of that effort to eventually trickle down into Google's consumer products (maybe a healthcare focused version of AutoML https://cloud.google.com/automl/). Google has already done a lot of work on the HIPPA side of things (https://cloud.google.com/security/compliance/hipaa/)
Being rules based isn't necessarily a bad thing or disingenuous. I develop healthcare AI products (ML/DL researcher) and we actually aim to be able to translate our models into a rules based engine (find a strong signal, interpret/understand model well enough to translate/embed into a rules engine, look for a new signal in our models, rinse + repeat). We end up deploying a mix of rules based and true ML based models into production but it may not be immediately obvious to the end user which type of model they are using.
Healthcare data can't be shared the way the browser histories, cell phone location data, etc. can. It's a completely different set of a rules that people have to play by (HIPPA for example). I build machine learning systems using healthcare claims and EHR data and without the direct cooperation of several large insurance companies (and access to their data) we'd be dead in the water. Even with access to their data there are incredibly strict limits to what we can and can't do with it. You can't just go out and collect healthcare data the way you can many other types of data.
It depends on the use case. Our work primarily revolves around extending Spark with custom pipelines, models, ensembles, etc. to be deployed into our production systems (petabyte scale). Scala was really the only way to go for us.
Not sure how easy it is to find outside of boutique pets stores (just happen to live near a fantastic one that delivers for free) but I highly recommend the Orijen brand. Have tried numerous other “premium” brands with my dogs over the years but no matter which brand I was using, I always managed to run into at least one vet than had less than stellar things to say about brand x, y, or z. Maybe it’s just coincidence but in the 7+ years I’ve been feeding my dogs Orijen I’ve yet to encounter a vet that anything other than positive things to say about the brand.
Having been privy to some of the contractual details of deals that Google has made with other medical centers, I’m betting that they probably got it for ”free”, as in they didn’t directly pay a set fee to the universities. Google most likely provided funding in the form of donations (tax write off), free cloud compute resources and/or cloud storage (write off), and the opportunity for university researchers to co-author high impact publications (everybody wins).
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).