Risk Adjustment Data Scientist

Blue Cross and Blue Shield of Kansas•Topeka, KS

About The Position

The Risk Adjustment Data Scientist is responsible for analyzing and interpreting complex data, developing models and algorithms, and deriving actionable insights to support the business. The Data Scientist will work closely with cross-functional teams to understand business needs and deliver data-driven solutions.

Requirements

High school diploma or equivalent required.
Bachelor's degree in computer science, statistics, mathematics, engineering, or a related field preferred. In lieu of degree, a minimum of an additional 3 years' experience as a data analyst, data scientist, or related role required.
Minimum of 3 years' experience as a data analyst, data scientist, or related role required.
Proficiency in programming languages such as Python or R.
Experience with data manipulation tools (e.g., SQL, SSIS, Python, pandas).
An understanding of data warehouse structures, data extraction tools, analytics software and methods, and data modeling.
Experience with big data and cloud platforms (e.g., AWS, Azure, Google Cloud).
Ability to identify and make sense of rich data sources, manage large amounts of data, merge datasets, ensure consistency of datasets, graphically portray data to gain insights, build statistical models, understand data, and model shortcomings, and effectively communicate findings.
Familiarity with data visualization tools (e.g., Tableau, Power BI).
Demonstrated experience with creating accessible, insightful, compelling, and branded data visualizations.
Strong analytical and problem-solving abilities.
Ability to interpret complex data and provide actionable insights.
Ability to apply, validate, and interpret results in a variety of machine learning contexts.
Excellent verbal, visual, and written communication skills.
Ability to present complex data and insights to non-technical stakeholders effectively.
Adept at identifying underlying mathematical and statistical assumptions of a given dataset or algorithm and implement and interpret results appropriately.
Ability to make decisions using independent judgment and creativity to resolve issues and overcome roadblocks.
Ability to work collaboratively with strong interpersonal skills and a proactive attitude.

Nice To Haves

Experience with and knowledge of the healthcare delivery system and the health insurance industry preferred.
Master's degree preferred.
Experience with the federal risk adjustment model and CMS (Centers for Medicare and Medicaid Services) reporting requirements preferred.
Familiarity with EDGE (external data gathering environment) server hardware and software specifications and experience with EDGE server data submissions preferred.
Understanding of data warehousing concepts and best practices preferred.

Responsibilities

Data Analysis & Interpretation: Analyze large and complex data sets from various sources to identify trends, patterns, and correlations. Translate data findings into actionable insights and recommendations.
Model Development: Develop, test, and deploy predictive and prescriptive models and scenario forecasting using machine learning and statistical techniques. Continuously evaluate and improve model performance.
Data Visualization: Create compelling data visualizations and dashboards to communicate findings and insights to both technical and non-technical stakeholders.
Collaborate & Communicate: Work with business stakeholders to understand their needs and translate them into data-driven solutions. Present findings and recommendations in a clear and concise manner. Support teammates by maintaining up-to-date, thorough, and clear documentation on data sources, preparation processes, mathematical models, dashboards, and other business intelligence activities.
Data Management: Ensure data quality, integrity, and security. Develop and implement processes for data collection, cleaning, and preparation.
Research & Innovation: Stay current with the latest industry trends, tools, and technologies. Explore and implement new methodologies to enhance data analysis capabilities.
Data Submission: Ensure accurate and timely collection of all risk adjustment data in compliance with applicable regulatory guidelines. Validate the completeness and integrity of data prior to submission, including diagnoses, demographic information, and provider documentation. Coordinate with internal teams and external partners to resolve data discrepancies or errors identified during the submission process.