Data Scientist

Position Overview: The Data Scientist is a key member of the project team responsible for analyzing complex healthcare datasets, developing sophisticated statistical models, and extracting valuable insights to support decision-making, public health research, and the achievement of project objectives. This role requires a deep understanding of data analysis techniques, strong statistical modeling skills, and the ability to work collaboratively with domain experts to derive meaningful conclusions from the data.

Responsibilities:

Data Analysis: Clean, preprocess, and analyze large-scale healthcare datasets, ensuring data quality, integrity, and readiness for advanced statistical modeling.

Statistical Modeling: Develop and implement statistical models to predict disease incidence, prevalence, outcomes, and other relevant metrics. Employ appropriate techniques, such as regression, clustering, time-series analysis, and machine learning, to derive actionable insights.

Health Research: Collaborate with domain experts, including public health professionals and healthcare researchers, to frame research questions and hypotheses in a public health context. Translate public health objectives into data-driven analyses.

Data Visualization: Create informative and compelling data visualizations to effectively communicate findings to both technical and non-technical stakeholders. Utilize visualization tools and techniques to convey complex patterns and trends.

Health Equity-Focused Analyses: Contribute to the design and execution of studies that prioritize health equity considerations, identifying disparities, and vulnerabilities among specific populations. Participate in the analysis of health outcomes in subgroups to address health disparities.

Model Evaluation: Assess the performance of developed statistical models, validate results, and refine models as needed. Continuously improve model accuracy and robustness through rigorous testing and validation procedures.

Interdisciplinary Collaboration: Work closely with other project team members, including data engineers, data analysts, public health experts, and information security specialists. Collaborate to ensure that data analysis aligns with the overall project objectives.

Research Methodologies: Stay updated on the latest advancements in data science, statistical analysis, and public health research. Implement best practices and innovative techniques to enhance the quality and depth of analyses.

Data Privacy and Security: Adhere to data privacy regulations and ethical considerations in all aspects of data handling and analysis. Collaborate with the information security specialist to ensure secure data practices.

Documentation: Document data analysis processes, methodologies, assumptions, and results in a clear and reproducible manner. Maintain comprehensive documentation for all analytical activities.

Qualifications:

Master's or Ph.D. in Data Science, Statistics, Epidemiology, or a related field with a focus on quantitative analysis. Demonstrated experience (typically 3+ years) in healthcare data analysis, statistical modeling, and data-driven research. Proficiency in programming languages such as R or Python for data analysis and modeling. Strong statistical knowledge and expertise in using statistical software and libraries for modeling (e.g., scikit-learn, TensorFlow, or equivalent). Experience with data visualization tools, such as Tableau or ggplot, to create meaningful and informative visualizations. Understanding of public health concepts, health outcomes, and health equity considerations. Ability to collaborate effectively with domain experts and communicate complex technical findings to non-technical stakeholders. Experience working with large-scale datasets, preferably in a healthcare or public health context. Familiarity with data privacy regulations (e.g., HIPAA) and ethical considerations in data analysis. Analytical mindset, with excellent problem-solving skills and attention to detail. Strong organizational skills, capable of managing multiple tasks and priorities.