Sr. Data Scientist

The Senior Data Scientist at MedeAnalytics is responsible for producing innovative solutions through exploratory data analysis from complex and high-dimensional data sets. They will utilize their skills in statistics, data modeling, advanced mathematics, and programming to design, develop, evaluate, and deploy robust solutions using data science, machine learning, and predictive modeling techniques. The Senior Data Scientist will collaborate with product teams and clients to translate real-world healthcare issues into well-defined problem statements and requirements, and will also be involved in data cleaning and exploration, feature engineering, model development, model deployment, model documentation, communication of results, and mentorship within the data science team.

• Collaborate independently with product teams and clients to translate real-world healthcare issues into well-defined problem statements and requirements to build out mathematical frameworks and data science solutions.
• Select appropriate datasets and data representation methods.
• Process, cleanse, and verify the integrity of data used for analysis and modeling.
• Use strong programming skills to explore, examine, and interpret large volumes of data in various forms.
• Develop data structures and pipelines to organize, collect, and standardize data used in data science workflow.
• Select influential features, as well as develop additional features, using machine learning techniques for use in model development.
• Design, develop, and validate data models and algorithms used for prediction, classification, pattern detection, and other insights related to healthcare issues.
• Develop documented, maintainable code.
• Develop and utilize unit tests to validate functional correctness and completeness, verify correct error handling, check input/output data, optimize performance, and identify and fix defects.
• Deploy and deliver AI/ML products as embedded algorithms into existing products or deploy them into production as microservices.
• Work closely with product development teams to design, build, manage, and test APIs.
• Collaborate with product teams and engineers to coordinate the implementation and QA of algorithms and other data science solutions.
• Continued evaluation and maintenance of models throughout their lifespan.
• Document projects including problem definition, data gathering and processing, detailed set of results, and analytical metrics.
• Use data visualization techniques to build presentations, dashboards, and reports to effectively communicate analytical results which drive insight, recommendations, and solutions.
• Present compelling, validated findings from exploratory and predictive data analysis to all levels of the organization, including peers, senior management, and customers.
• Peer review data science code and other product artifacts to ensure technical, logical, and procedural correctness. Validate assumptions and review for hidden biases.
• Serve as a resident data expert and share best practices/approaches for statistics, machine learning techniques, data modeling, simulation, and advanced mathematics.
• Provide mentorship and guidance to other members of the data science team.

• Exceptional skills in statistics, data modeling, advanced mathematics, and programming
• Ability to independently complete responsibilities
• Collaborate with product teams and clients to translate real-world healthcare issues into problem statements and requirements
• Select appropriate datasets and data representation methods
• Process, cleanse, and verify the integrity of data used for analysis and modeling
• Strong programming skills to explore, examine, and interpret large volumes of data
• Develop data structures and pipelines to organize, collect, and standardize data
• Select influential features and develop additional features using machine learning techniques
• Design, develop, and validate data models and algorithms for prediction, classification, pattern detection, and other healthcare insights
• Develop documented, maintainable code
• Utilize unit tests to validate functional correctness and completeness, optimize performance, and identify and fix defects
• Deploy and deliver AI/ML products as embedded algorithms or microservices
• Work closely with product development teams to design, build, manage, and test APIs
• Collaborate with product teams and engineers to implement and QA algorithms and data science solutions
• Continuously evaluate and maintain models throughout their lifespan
• Document projects including problem definition, data gathering and processing, results, and analytical metrics
• Use data visualization techniques to communicate analytical results effectively
• Present findings from exploratory and predictive data analysis to all levels of the organization
• Peer review data science code and other product artifacts
• Serve as a resident data expert and share best practices/approaches for statistics, machine learning techniques, data modeling, simulation, and advanced mathematics
• Provide mentorship and guidance to other members of the data science team
• Degree with a quantitative element (e.g. mathematics, statistics, computer science)

• Competitive base salary range of USD $100,000 - $150,000
• Opportunity to work with a leader in healthcare analytics
• Innovative solutions that enable measurable impact for healthcare payers and providers
• Actionable insights that improve financial, operational, and clinical outcomes
• Opportunity to uncover millions of dollars in savings annually
• Collaborative work environment with product teams and clients
• Opportunity to work with complex and high-dimensional data sets
• Opportunity to use advanced data science, machine learning, and predictive modeling techniques
• Exceptional skills in statistics, data modeling, advanced mathematics, and programming
• Independence in completing responsibilities
• Opportunity to work on real-world healthcare issues
• Data cleaning, exploration, and verification
• Strong programming skills for data analysis and interpretation
• Development of data structures and pipelines
• Feature engineering using machine learning techniques
• Design, development, and validation of data models and algorithms
• Documentation of projects and analytical metrics
• Communication of analytical results through data visualization techniques
• Opportunity to present findings to all levels of the organization
• Peer review and validation of data science code and artifacts
• Mentorship and guidance to other members of the data science team