Lead Data Scientist ( (Not accepting OPT & and H1B))

Role: Lead Data Scientist

• Lead high-priority and complex data science projects that have a significant organizational impact.
• Analyze structured and unstructured datasets using advanced statistical and analytical techniques.
• Develop custom data models, algorithms, and predictive solutions to address complex business problems.
• Apply machine learning and statistical models to key business metrics and operational challenges.
• Perform research, data analysis, modeling, data mining, visualization, and pattern analysis.
• Develop and test hypotheses and communicate findings in a clear, precise, and actionable manner.
• Maintain existing analytical models and evaluate model performance and goodness of fit.
• Identify opportunities to improve operational efficiency, productivity, scalability, and business outcomes through data.
• Work closely with cross-functional teams to identify, investigate, and resolve complex data issues.
• Gather requirements and translate business needs into analytical solutions.
• Provide technical leadership, coaching, and mentoring to other data scientists.
• Train broader teams on data science methodologies, tools, and developments.
• Assist in evaluating data science vendors, technologies, platforms, and tools.
• Lead multiple projects simultaneously while managing competing priorities and deadlines.
• Troubleshoot complex analytical and data-related issues and recommend appropriate solutions.
• Support strategic and operational decision-making through advanced data insights.
• Perform other duties and projects as assigned.

• Advanced data science and analytics
• Machine learning and predictive modeling
• Advanced statistical analysis
• SQL and database management
• Data mining and data visualization
• Structured and unstructured data analysis
• Statistical modeling and hypothesis testing
• Time-series forecasting
• Regression analysis
• Clustering and classification
• A/B testing
• Data storytelling and visualization
• Business and technical requirements gathering
• Project leadership and management
• Cross-functional collaboration
• Technical mentoring and team leadership
• Problem-solving and analytical reasoning
• Executive and stakeholder communication

• Bachelor's degree in Science, Engineering, Computer Science, Mathematics, Statistics, or a related STEM field required.
• Master's degree in Data Science preferred.

• None required.

• Minimum 7 years of professional experience in Data Science.
• Experience in a hospital, healthcare, medical informatics, healthcare information technology, healthcare finance/revenue cycle, or Electronic Health Record (EHR) data environment is preferred.
• Strong business analytical skills, including process analysis, modeling, spreadsheets, and workflow analysis.
• Strong technical, mathematical, and analytical capabilities.
• Deep understanding of machine learning techniques, including:
• Clustering
• Decision tree learning
• Artificial neural networks
• Predictive modeling
• Classification techniques
• Advanced knowledge of statistical concepts and techniques, including:
• Regression
• Statistical testing
• Probability and distributions
• Hypothesis testing
• A/B testing
• Advanced understanding of the data science project lifecycle.
• Strong programming skills and experience with statistical analysis tools.
• Advanced knowledge of SQL and database management.
• Experience researching and resolving data issues involving large, complex, and incomplete datasets.
• Exceptional analytical and problem-solving skills.
• Ability to interpret and communicate complex analytical results.
• Strong project management skills and ability to independently manage multiple projects.
• Strong written and verbal communication skills with the ability to communicate effectively with technical and non-technical audiences.
• Ability to work with minimal supervision in a fast-paced, multidisciplinary environment.
• Strong customer-service orientation and commitment to producing high-quality analytical work.
• Ability to manage challenging stakeholder situations and provide effective solutions.

• Hospital or healthcare data
• Electronic Health Records (EHR)
• Healthcare IT
• Medical informatics
• Healthcare finance
• Revenue cycle data
• Clinical or operational healthcare analytics