Data Scientist (Machine Learning Engineer) - Remote
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, as well as 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 allows 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 the development of the most accurate financial, clinical and behavioral models in the industry.
We’re looking for a well-rounded data scientist/machine learning engineer to join the Data Science team at Certilytics. At a high level you'll be responsible for designing and running experiments to bring the latest deep learning advances from the literature to our products. From a day to day perspective you will be tasked with building deep learning models and pipelines for deployment and contributing to proprietary machine learning libraries. The ideal candidate will have a strong background in multi-modal modeling, natural language processing (Seq2Seq, NMT, and/or text based classification models) and familiarity with the inner workings of recurrent and transformer neural networks.
What You'll Accomplish
- Research new ideas, new models, and new methodologies. Develop proofs of concept and answer research questions relevant to Certilytics and our models.
- Build production grade deep learning pipelines.
- Write research-quality documentation to demonstrate the theoretical grounding of our work.
What You’ll Need
- Data science related programming skills as evidenced by 1-3 years of hands on experience with deep learning (Tensorflow) and scripting languages, preferably Python. You should also be familiar with Git and Markdown and possess a solid grasp of algorithms and data structures.
- Graduate level statistical and mathematical background via a Masters in a quantitative discipline (eg. statistics, mathematics, physics, computer science, operations research).
- Strong writing skills in academic-style writing.
- Ability to collaborate across teams and answer complex questions that arise from various teams.
- Ability to distill difficult concepts down to layman’s terms.
- Comfort with data big and small.
How to stand out!
- You have implemented a deep learning model pipeline in production.
- You have published in an academic journal, conference, or similar.
- Domain experience in a healthcare-related industry.
- You have performed custom deep learning model or layer development outside of what is available in the standard library.
- Familiarity with cloud technologies, containers (Docker and Kubernetes), distributed computing, Kubeflow and/or Seldon.
- Access to one of the largest 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 have regular in-person working sessions for all of those little things that are hard to accomplish over Teams.