The Senior Data Scientist will produce innovative solutions driven by exploratory data analysis from complex and high-dimensional data sets. The Senior Data Scientist uses a flexible, analytical approach to design, develop, evaluate, and deploy robust solutions leveraging innovations in data science, machine learning, and predictive modeling techniques. To do this job successfully, you need exceptional skills in statistics, data modeling, advanced mathematics, and programming. The Senior Data Scientist should be able to independently complete responsibilities. 

Essential Duties and Responsibilities:

• Problem Definition
• 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. 
• Data Cleaning and Exploration
• 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
• Feature Engineering
• Select influential features, as well as develop additional features, using machine learning techniques for use in model development.
• 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.
• Model Deployment
• 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.
• Model Documentation
• Document projects including problem definition, data gathering and processing, detailed set of results, and analytical metrics.
• Communication of Results
• 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.
• Mentorship
• 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.

Essential Education, Experience, and Interests:

• Degree with a quantitative element (e.g. mathematics, statistics, economics, engineering, computer science, applied math, etc.) or 5+ years equivalent job experience.
• Strong proficiency with Python, Jupyter Notebooks, and standard Python data science libraries including, but not limited to: Pandas, Numpy, and Scikit-Learn
• Demonstrates proficiency in several areas of data modeling, machine learning algorithms, statistical analysis, data engineering, and data visualization. 
• Experience with cross-functional collaboration and project ownership.
• Experience building microservices, preferably using Flask and Gunicorn.
• Strong communication competencies to include presentations and delivery of complex quantitative analyses in a clear, concise, and actionable.
• Proficient understanding of Git.
• Knowledge of health care terminology. (e.g. HRGs, diagnosis/procedure codes, etc.) Experience working with both payer and provider data preferred.
• Preferred experience with Amazon Sagemaker, and Oracle
• Experience with SonarQube, unit test, coverage, and nosetests for code quality testing preferred.
• Experience with Docker, Postman, and REST APIs preferred.
• Experience with Vertica, MongoDB preferred.

Compensation Information:

• Base Salary Range: USD $100,000 - $150,000

**Individual compensation packages are based on various factors unique to the candidate, including skill set, relevant experience, qualifications, and other job-related reasons.

This job description reflects management’s assignment of essential functions. Nothing in this job description restricts management’s right to assign or reassign duties and responsibilities to this job at any time.