Staff Data Scientist

About the role

We are seeking a Staff Data Scientist to lead high-impact data initiatives centered on Master Data Management, record linkage, and entity resolution across large, complex enterprise datasets. In this role, you will act as the primary domain expert for unifying siloed data into highly trusted, well-governed data products. You will work cross-functionally to design predictive models, build semantic data layers, and translate complex probabilistic outputs into clear, actionable logic for executive leadership and clinical stakeholders.

Key Responsibilities

Entity Resolution & ML Modeling: Develop and deploy deterministic, probabilistic matching, and graph-based entity resolution models to solve large-scale record linkage challenges across enterprise data silos.

Analytics & Semantic Layer: Design data pipelines, establish metric frameworks, and build semantic layers to ensure high data quality, integrity, and robust validation.

Cross-Functional Collaboration: Partner with data governance, data engineering, and business teams to translate complex use cases into production-grade data science workflows and optimized pipelines.

Data Visualization & Storytelling: Build dashboards and visual frameworks to clearly communicate complex algorithmic outputs and model insights to non-technical stakeholders and executive leadership.

Technical Leadership: Provide technical guidance and mentorship to junior analysts, enforcing software development and data pipeline best practices.

Qualifications & Skills

Experience: 8+ years of hands-on experience in data science, advanced analytics, and data engineering.

Entity Resolution Expertise: Proven track record with record linkage, deterministic/probabilistic matching models, and graph-based entity resolution at enterprise scale.

Technical Proficiency: Expert-level Python and SQL skills for writing production-grade, modular code. Experience with root-cause analysis on training/test samples to resolve model anomalies.

Data Stack & Tools:

Data Warehousing & Cloud: Databricks, AWS (Glue, Redshift).

Visualization: Power BI, Datadog, Tableau.

Workflow: Git, GitHub, Jira, Confluence.

Education: Bachelor’s, Master’s, or Ph.D. in Data Science, Statistics, Computer Science, or a related field.

Communication: Exceptional ability to distill complex analytical findings into actionable business insights.