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.
yep, Metaflow tracks everything: Your code, dependencies, and the internal state of the workflow automatically.
A big difference between Metaflow and other workflow frameworks for ML is that Metaflow doesn't only execute your DAG, it helps you to design and implement the code that runs inside the DAG. Many frameworks leave these details to the data scientist to decide.
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.
We're building something just like that at qri (https://qri.io) a free and open source dataset version control system. Right now all datasets on qri are public by default, but we're working toward supporting. encryption and private networks.
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.
I didn't mean it as being disingenuous - that's precisely the value that was sold and if you could do the proper "knowledge engineering", it worked well. It's just interesting to me having seen the previous turn of the AI hype wheel, how much is being repeated.
Another interesting thing was the transition from special purpose hardware - Lisp machines - to C code on commodity platforms. A contrast from today's ML moving in the other direction.
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.
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).
The likes of INTERNIST, CADUCEUS, and MYCIN have been around and provably accurate starting in the late 70s through the mid-80s. MYCIN even arguably sparked the 1st AI boom. But there were ethical issues with computer-aided diagnosis that I'm not sure have been solved/overcome.
Perhaps the current startup generation can get past them with Zuckerberg, Kalanick and Holmes as role models. :)
