Data Scientist

Core Characteristics Achievement-oriented Enjoys taking on challenges, even if they might fail. Innovative – Prefers working in unconventional ways or on tasks that require creativity with multivariable inputs and uncertainties. Resource maximizing – Identifies and leverages team resource strengths, while maintaining awareness and pushing for needed growth opportunities. Autonomous/ independent – Enjoys working with little direction. Dynamically detail oriented – Has the awareness of the need and ability to go deep without getting lost, while staying grounded with the bigger picture. Critical thinker – Applies logic, reason, and rationale to a given problem and/ or proposed solution. Dependable – More reliable than spontaneous. Flexible – Ability to manage competing priorities in a fluid environment. Prioritizing – Effective with prioritization, goal setting, and time management. Communicator – Excellent in communications, interpersonally, and in writing. Key Responsibilities: Analyze and interpret complex datasets to uncover valuable insights. Develop and implement Machine Learning (ML) models and algorithms. Collaborate with the team to design and execute data experiments. Utilize cloud platforms for data storage, processing, and analysis. Present findings and insights to both technical and non-technical stakeholders Stay updated on industry trends and advancements in data science, with a focus on healthcare informatics. Analyze complex healthcare data sets to identify trends, patterns, and insights. Create and present reports, dashboards, and visualizations to communicate findings to stakeholders. Isolate healthcare trend drivers and financial impacts associated with client medical claims, pharmacy claims, and other types of healthcare data. Perform varying types of analysis including medical carrier network valuations, discount analysis, and benchmarking. Conduct complex analyses adhering to best practice standards by selecting appropriate data sources, developing assumptions, recognizing considerations, and establishing recommendations. Identify opportunities for process improvement and efficiency through data analysis. Resolve unreasonable results, suboptimal solutions, and data anomalies based on experience and professional judgment. Act as a technical expert and advise on strategic data mining techniques used to identify new relationships or patterns in data. Build predictive models to accurately analyze potential outcomes that are likely costs / savings for a given initiative or event(s). Translate, document, and present approaches, processes, and results of complex modeling and statistical analysis into layperson terms for diverse, internal and external audiences. Qualifications: Degree in computer science, Statistics, Data Science, or a related field. Strong working knowledge of data manipulation and analysis libraries (e.g. Pandas, NumPy, scikit-learn) Familiarity with machine learning (ML) concepts and algorithms. Analyze and interpret complex healthcare datasets to extract valuable insights. Experience with data visualization tools (e.g. Matplotlib, Seaborn, tableau, PowerBI) Basic understanding of databases systems and SQL Experience with cloud platforms (e.g., AWS, Azure or Google Cloud) in a healthcare and biomedical data (ex., clinical and life-sciences) context. Experience with AI/ML frameworks (e.g., TensorFlow, PyTorch) and familiarity with generative AI tools for innovative data-driven solutions. Excellent problem-solving and analytical skills. Eagerness to learn and adapt to new technologies and methodologies. Must possess strong analytical skills with the ability to collect, organize, analyze, and disseminate information with attention to detail and accuracy. Ability to generate information quickly and manage multiple projects at once. Strong analytical skills with the ability to collect, organize, analyze, conceptualize, problem solve, and disseminate information confidently and collaboratively with attention to detail, accuracy, timeliness. Experience in building and deploying highly scalable systems, algorithms, and tools on platforms to support machine learning and deep learning solutions Experience with Azure/AWS tools and technology