Data Scientist
The Role
The Data Scientist will provide insights on the design and execution of health outcomes research studies, including retrospective database analysis, and meta-analysis. The Data Scientist will work closely with the Director of Analytics & Informatics to develop and implement advanced analytics techniques, medical informatics and predictive modelling methodologies to support companies business needs.
Responsibilities
Understand key business drivers and support development of an analytical framework that delivers actionable insights for company and our portfolio companies.
Work collaboratively with product teams to ensure a shared understanding of business objectives and define what new insights are needed to support these objectives.
Become a trusted analytics partner for company by identifying areas that will enhance the organization s analytical toolkits to deliver data-driven insights for leadership, influence decision making, drive revenue and growth, while bridging knowledge gaps.
Participate in discussions with clinical faculty, subject matter experts, business leaders to discuss current issues in medical practice and develop research hypotheses.
Hands-on data extraction and database analysis using the electronic medical record and other relevant data sources.
Serve as an expert consultant regarding advanced data mining and predictive modeling methods, as well as application of scientific principles of knowledge discovery.
Contribute to development and review of study protocols, Analysis plans, and other documents related to the execution of clinical, health economics and outcomes research studies and models.
Develop strong problem-solving skills in the creation and interpretation of quantitative analyses and predictive models and assist the Director in the development of Analysis plans.
Demonstrate ability to independently design rigorous clinical, financial and quality analyses grounded in data science.
Contribute to the write-ups, including relevant portions of manuscripts, abstracts, posters, slide presentations. Author and present studies at scientific conferences and other appropriate venues on the behalf of the study team, as needed.
Qualifications
Education & background
PhD/MD with training or equivalent terminal degree in health economics, data science, statistics, computer science or related field.
Demonstrated expertise in relevant applied analytical methods in healthcare (payor/provider).
At least 5 years of experience in developing, implementing and overseeing models related to health services/ outcomes research and medical information programs or related work experience. A comparable combination of education and experience will be considered in lieu of the above- stated qualifications.
Demonstrate prior independent application of data science methods specifically to healthcare industry data at the expert level.
Ability to leverage cutting-edge data science experience from other industries (e.g. population segmentation, risk analysis, optimization analysis, real-time analytics) to advance healthcare analytics will be strongly considered in lieu of health care experience.
Advanced Analytics Skillset
A high level of proficiency in clinical and scientific research methodologies to generate research questions, query complex clinical data to conduct descriptive and predictive analysis that create new insights to address companies business needs.
Expert ability to extract and manipulate data utilizing SQL from large, complex data sets without supervision.
Expert in using SAS or R or other statistical packages to conduct statistical analysis and modeling.
Ability to independently select and apply multiple advanced data mining, statistical analysis and predictive modeling methods.
Experience with observational study designs, including fluency in methods for confounding control (e.g. propensity scores), tactics for missing data (e.g. imputation), and other statistical methods relevant to the study of large real-world data sets.
Knowledge of hands-on experience with data visualization tools (e.g Tableau, Power BI, etc.)
Communication & Stakeholder interaction
Effective data analysis and interpretation skills with ability to draw and present quantitative conclusions leveraging graphs, and other visualizations to enable rapid understanding of clinical data to deliver business insights.
Ability to evaluate, interpret & synthesize scientific data, and to verbally present and critically discuss clinical trial and published data in internal and external meetings.
Present updates (written and/or verbal) on business results using insights from dashboards, reports, and/or ad-hoc analysis.
Exceptional interpersonal skills, and entrepreneurial orientation characterized by pragmatism, independence, self-determination, and an agile, flexible behavior style.
Excellent communicator with ability to prepare and deliver clear scientific and business communication materials (documents, presentations) for internal and external facing activities.
Ability to influence senior executives through effective executive communication of data science methods and study results.
Business
Demonstrated understanding of the differences between business requirements, scientific rigor and technical constraints with ability to distill complex issues and ideas down to simple comprehensible terms.
Demonstrated understanding of financial metrics and cost efficiencies that have a positive business impact.
Project Management
Excellent time management, organizational and prioritization skills with ability to balance multiple priorities with experience in project management including proposal or grant preparation, developing project plans and Gantt charts, spend plans, and managing work execution including earned value.
Quick learner with ability to gain deep understanding of companies processes, cross service interactions and interdependencies.
Self-driven, scientifically curious individual who thrives in a high pace, and rapidly evolving business environment that supports entrepreneurs and founders.
Preferred
Experience with Agile principles and development methodology.
Broad knowledge of advanced analytics, research infrastructure, discovery platforms and the application of artificial intelligence/machine learning for health.
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