What are the responsibilities and job description for the Data Scientist II - Claims position at Lensa?
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Summary: Performs analysis of claims data, complex clinical and other varied data to develop innovations for pressing healthcare delivery challenges through visualization, machine learning, optimization, and statistical inference solutions. Maintains relationships with payers and third party administrators (TPAs) to assure high quality claims data feeds. Analyzes and models structured data and implements algorithms to support analysis using advanced statistical and mathematical methods. Develops descriptive and predictive models, from conception to completion and provides long-term support. Performs exploratory data analysis, generates and tests working hypotheses, and uncovers interesting trends and relationships. Documents and shares findings in line with scientific best practices for both technical and nontechnical audiences.
Key Responsibilities
Licensure/Certification: Epic certification at time of hire or must obtain within 6 months after hire. Formal training in the use of an enterprise analytics software solution. Licensed healthcare professional desirable.
Experience: 2-5 years of relevant work experience in healthcare preferably in a claims related dsicipline. Previous experience with Epic including Epic certifications, SQL, Python (or similar) and Power BI are preferred. Experience with cloud computing platforms (e.g., AWS, Azure, Google Cloud, and Databricks) is desired. Level II Data Scientist is highly skilled across multiple Data Science disciplines. Is able to independently own and solve a well-defined business problem where the solution has not yet been outlined or approach is unclear; is able to provide high quality solutions (e.g., accurate, efficient) which do not require refinement to deliver value; is able to solve difficult problems that require a range of data science methodologies combined with subject matter expertise. Strong analytical and problem-solving skills, and ability to work on moderately complex projects as well as proficiency in data analysis and visualization tools (e.g., Python, SQL, Tableau) is expected. Excellent collaboration and communication skills to maintain TPA relationships and to convey technical findings to non-technical stakeholders is a must. Must be skilled at problem solving, have strong organization skills, attention to detail, and demonstrated ability to understand complex issues and communicate these issues to co-workers. Ability to communicate, both verbally and written, effectively with technical team, as well as managers and end users.
Summary: Performs analysis of claims data, complex clinical and other varied data to develop innovations for pressing healthcare delivery challenges through visualization, machine learning, optimization, and statistical inference solutions. Maintains relationships with payers and third party administrators (TPAs) to assure high quality claims data feeds. Analyzes and models structured data and implements algorithms to support analysis using advanced statistical and mathematical methods. Develops descriptive and predictive models, from conception to completion and provides long-term support. Performs exploratory data analysis, generates and tests working hypotheses, and uncovers interesting trends and relationships. Documents and shares findings in line with scientific best practices for both technical and nontechnical audiences.
Key Responsibilities
- Data Collection and Cleaning: Owns the claims data stream from payers and TPAs monitoring quality reports and collaboratively resolving issues to ensure data quality and integrity.
- Data Analysis: Applies statistical and machine learning techniques to identify patterns, trends, and insights in the data.
- Model Development: Assists in the development of predictive models, algorithms, and statistical analyses to solve specific business problems.
- Data Visualization: Creates clear and insightful data visualizations to communicate findings to non-technical stakeholders.
- Collaboration: Collaborates with internal and external cross-functional teams, including clinical subject matter experts, engineers and developers, product managers, and business analysts, to provide data-driven solutions.
- Documentation: Maintains clear and organized documentation of analysis methodologies and results.
- Continuous Learning: Stays up-to-date with industry trends and emerging data science techniques to improve skills and knowledge.
Licensure/Certification: Epic certification at time of hire or must obtain within 6 months after hire. Formal training in the use of an enterprise analytics software solution. Licensed healthcare professional desirable.
Experience: 2-5 years of relevant work experience in healthcare preferably in a claims related dsicipline. Previous experience with Epic including Epic certifications, SQL, Python (or similar) and Power BI are preferred. Experience with cloud computing platforms (e.g., AWS, Azure, Google Cloud, and Databricks) is desired. Level II Data Scientist is highly skilled across multiple Data Science disciplines. Is able to independently own and solve a well-defined business problem where the solution has not yet been outlined or approach is unclear; is able to provide high quality solutions (e.g., accurate, efficient) which do not require refinement to deliver value; is able to solve difficult problems that require a range of data science methodologies combined with subject matter expertise. Strong analytical and problem-solving skills, and ability to work on moderately complex projects as well as proficiency in data analysis and visualization tools (e.g., Python, SQL, Tableau) is expected. Excellent collaboration and communication skills to maintain TPA relationships and to convey technical findings to non-technical stakeholders is a must. Must be skilled at problem solving, have strong organization skills, attention to detail, and demonstrated ability to understand complex issues and communicate these issues to co-workers. Ability to communicate, both verbally and written, effectively with technical team, as well as managers and end users.
