Demonstrated project management
Desire to learn, be challenged, and frequently address new problems
In depth statistical knowledge including familiarity with stochastic models, regression models, time series, survival models, and Monte Carlo methods
Experience building and running machine learning models
Strong communication skills to translate complex mathematical results and concepts to internal and external audiences who often may not have a statistics or data background
Knowledge of healthcare data, finance, and regulation
Ability to define problems for clients and build solutions working with both internal and external (client) resources
Proficiency in statistical programming languages such as R and Python
ASA/FSA or progress towards SOA credentials
Experience with Medicare data
Experience with reimbursement models
Experience with risk adjustment
People management experience
About the Company
We are a small (~25 person) actuarial, health economics, and data science company that quantifies value creation in healthcare and assists in driving efficiencies in healthcare delivery. In so doing, we play an important role in advancing the Triple Aim of Healthcare: better care for individuals, better health for populations, and lower per capita costs. With over 60 peer reviewed articles and 2 textbooks, we are well published in the space of healthcare predictive analytics and outcomes measurement. Our business control cycle starts with quantifying opportunity for improvements, then moves to retrospectively measuring actual outcomes due to disruptions in the medical ecosystem, and then moves to optimizing those disruptions through the use of big data and predictive analytics. Our clients include medical device companies, care management programs, provider groups, payers, and governments.
As our predictive models have scaled, we’ve recognized the need to add data engineering expertise to our team to assist with managing the data warehouses we’ve built as well as expanding our capabilities in this area.