Job Description Insight Global is seeking a Staff Data Scientist to join a critical project with one of the largest consulting and advisory firms in the world. This role will lead data-driven initiatives focused on generating actionable insights, developing predictive models, and enabling data-centric decision-making across healthcare business domains. The Staff Data Scientist will serve as a key subject matter expert for entity resolution, helping create trusted and well-governed master data products that support enterprise-wide analytics and reporting. This individual will work closely with data governance leaders, engineers, and business stakeholders to solve complex data challenges and deliver scalable, production-ready solutions. Key Responsibilities Analyze large, complex healthcare datasets to identify trends, patterns, and actionable business insights. Develop, implement, validate, and monitor statistical and machine learning models in production environments. Lead enterprise-scale record linkage, entity resolution, deterministic matching, probabilistic matching, and graph-based data initiatives. Conduct root cause analysis of training and test data to identify anomalies and improve model performance. Establish validation frameworks and testing methodologies to ensure data quality, model reliability, and data integrity. Design and build scalable data pipelines that support semantic-layer development and trusted master data products. Develop and refine KPIs, performance metrics, and reporting processes to measure business success. Partner with data governance teams, business stakeholders, and data engineers to translate requirements into data science solutions. Create dashboards, visualizations, and executive-level presentations that communicate complex analytical findings to technical and non-technical audiences. Mentor junior data scientists and analysts while promoting best practices in machine learning, analytics, data quality, and governance. We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.

To learn more about how we collect, keep, and process your private information, please review

Insight Global's Workforce Privacy Policy:

https://insightglobal.com/workforce-privacy-policy/. Skills and Requirements 8+ years of experience in Data Science, Advanced Analytics, Data Engineering, or a related field within enterprise environments. 5+ years of hands-on experience developing and deploying machine learning, predictive modeling, and statistical analysis solutions using Python and SQL. 3+ years of experience with record linkage, entity resolution, deterministic/probabilistic matching, and graph-based data methodologies. 3+ years of experience building and optimizing data pipelines using Databricks, AWS Glue, Amazon Redshift, and cloud-based data platforms. Experience developing production-ready data products, model validation frameworks, and performance monitoring processes. Bachelor's degree or higher in Data Science, Statistics, Computer Science, Mathematics, or a related quantitative discipline. Experience working with healthcare data, healthcare analytics, or other highly regulated data environments. Experience with Power BI, Datadog, Git/GitHub, Jira, and Confluence. Experience mentoring technical teams and contributing to data governance, master data management, or semantic-layer initiatives.