Associate, NCDR Data Scientist

Job Description

The data scientist plays a key role in advancing the analytical engine behind the National Cardiovascular Data Registries (NCDR). This role drives the development of clinical datasets, powers quality and performance measure reporting, and strengthens ACC’s registry science through advanced analytics, SQL‑based measure implementation, and emerging AI‑driven solutions. You’ll help shape smarter, more efficient processes by validating and optimizing registry data and models, ensuring accuracy and high‑quality outputs that directly support clinicians and participants nationwide. Success in this role calls for strong analytical thinking, problem‑solving skills, and the ability to stay organized in a dynamic, matrixed environment while collaborating closely with the NCDR Data Science and Analytics team.

Responsibilities

Dataset Development Functions:

• Perform frequency and other possible analysis, e.g., descriptive statistical, on data element submission rates and clinical metric reporting application to inform dataset development by internal staff and volunteer workgroups.
• Conduct assessments (e.g. rule-based, correlation analysis) to identify redundant data elements and recommend reductions as well as standardization across datasets.
• Manage registry data elements for automated upgrades.
• Develop ongoing monitoring views into data submission rates to inform threshold submission requirements, shifts in clinical characteristics at the facility and patient level, and other dataset-based analysis, such as for clinical research purposes, as well as potential correlations within data element submission completeness to complexity of patient populations to support customer coding.
• Perform analysis to support clinical measurement staff and volunteer workgroups and Steering committees develop and report meaningful measures as well as identify candidate measures to be sunset due to low reporting frequencies associated with low data element capture completeness provided in submissions.
• Collaborate with clinical measure development and IT teams to propose data visualization approaches to increase ease of interpretability of measures for customers.
• Develop ongoing monitoring of clinical measures reported across registry dashboards to identify to extent reasonable variations in outcomes.

Measure Development Functions:

• Perform analysis to support clinical measurement staff and volunteer workgroups and Steering committees develop and report meaningful measures as well as identify candidate measures to be sunset due to low reporting frequencies associated with low data element capture completeness provided in submissions.
• Collaborate with clinical measure development and IT teams to propose data visualization approaches to increase ease of interpretability of measures for customers.
• Develop ongoing monitoring of clinical measures reported across registry dashboards to identify to extent reasonable variations in outcomes.

Clinical Informatics Function:

• Collaborate with staff and volunteer committee and work group members in identify opportunities to align with external structured terminologies and/or apply large language modeling (LLM) approaches to ease effort associated with data capture by customers for submission to NCDR.

• Assist on projects as identified, such as to explore novel mechanisms to import clinical data to expand view of patient care and outcomes including applications to clinical measurement reporting; or to support efficiencies in data export out of electronic medical record systems or other data environments, e.g., lakes, for customers or third-parties, e.g., certified software vendors.

General:

• Learn the NCDR Common Data Model (CDM) structure to be able to interpret code and conduct quality-checks for measures and models.

• Monitor and analyze AI industry trends and identify opportunities to apply AI techniques to improve internal efficiency assess customer adoption of AI in registry contexts.

• Use analytic approaches through tools such as SQL and Excel to identify opportunities for process streamlining.
• Assist with the interpretation, responses, and presentation of results to internal and external stakeholders such as the CMS Consensus Based Entity, regulatory and federal agencies and other health authorities for all measure types (process, outcome, risk-modeled, composite, etc.).
• Develop and maintain strong working relationships with internal and external stakeholders to facilitate collaboration and drive execution of program deliverables.
• Create and maintain standard operating procedures and technical documentation.

Qualifications

Required:

• Masters with 3+ years relevant experience or Bachelors with 4+ years relevant experience training in data management, data quality, health care, computer science, or a related field.
• Hands-on experience building, validating and deploying predictive models (including both statistical and machine learning models).
• Strong experience using data analytics and/or visualization software and tools such as PowerBI, SQL, SAS, Python, or similar products.
• Excellent problem solving and analytical skills.
• Demonstrated AI literacy, including understanding of generative AI, RAG, and staff‑facing productivity AI tools.
• Strong project management skills with the ability to manage multiple tasks simultaneously and set priorities under strict deadlines.
• Demonstrated ability to embrace changing organizational needs, shifting priorities, and evolving responsibilities.
• Client service orientation and interpersonal skills with the ability to build effective relationships with diverse constituents and work across teams, departments, and functions.
• Ability to self-initiate, advance work with minimal direction, and follow through on projects.

• Advanced working knowledge of Microsoft Office Products (Word, Excel, PowerPoint), familiarity with SharePoint and version control software (e.g., Git).

Desired:

• Familiarity with protected health information (PHI) data and privacy requirements.
• Experience developing and working with clinical measures and/or clinical informatics.
• Advanced degree and/or additional experience working with health data.
• Experience in coordinating cross-functional teams to implement AI solutions in business process and operations.
• Knowledge of natural language processing (NLP) or large language modeling (LLM) techniques and healthcare AI applications.
• Understanding of ethical AI principles and regulatory compliance in healthcare.

About Us

PI282421073