Sr. Data Analyst

Commence•Baltimore, MD

11h

About The Position

At Commence, we’re the start of a new age of data-centric transformation, elevating health outcomes and powering better, more efficient process to program and patient health. We combine quality data-driven solutions that fuel answers, technology that advances performance, and clinical expertise that builds trust to create a more efficient path to quality care. With human-centered, healthcare-relevant, and value-based solutions, we create new possibilities with data. We provide proof beyond the concept and performance beyond the scope with a focus on efficiencies that transform the lives of those we serve. With a culture driven by purpose, straightforward communication and clinical domain expertise, Commence cuts straight to better care.

Requirements

Bachelor’s degree in Health Informatics, Public Health, Statistics, Computer Science, Data Science, or a related field; Master’s degree preferred.
7+ years of experience working with healthcare data, including EHR, claims, registries, or public health datasets.
Strong proficiency in SQL and at least one analytics-oriented programming language such as Python or R.
Experience building analytical datasets, dashboards, or reporting frameworks using tools such as Excel, Tableau, Power BI, or QuickSight.
Experience with Databricks, Snowflake, or other modern cloud/notebook-based data platforms.
Familiarity with healthcare data standards such as FHIR, HL7, ICD, CPT, and LOINC, and experience working with healthcare quality or performance measures.
Demonstrated ability to synthesize complex data and present findings to both technical and non-technical audiences.
Strong data validation, data governance, and documentation practices.
Excellent communication and collaboration skills, with the ability to work effectively with both technical and non-technical teams.
Strong problem-solving skills, attention to detail, and a commitment to continuous learning and improvement.
Experience working in a healthcare or regulated environment.
Experience working in Agile development environments and collaborating within cross-functional product or engineering teams.

Nice To Haves

Understanding of data governance and security frameworks relevant to healthcare such as NIST or HITRUST.
Prior experience working with government agencies such as CMS, VA, or DoD, or with payer or provider organizations.
Knowledge of healthcare delivery systems, policy frameworks, and value-based care programs.
Experience designing or supporting dashboards or reports for operational, clinical, or executive audiences.
Master’s degree or professional certifications in Data Analytics, Health Informatics, or a related field.

Responsibilities

Analyze and interpret large-scale healthcare datasets (e.g., claims, EHR, FHIR, registries) to identify trends, outliers, and key performance indicators that inform clinical, operational, and strategic initiatives.
Lead complex analytical efforts to evaluate healthcare performance, utilization patterns, and program outcomes across diverse datasets.
Collaborate with data engineers, product managers, and domain subject matter experts to define data requirements, metrics, and quality controls that support reliable analytics and reporting.
Build and maintain curated datasets, data models, and dashboards for internal and external stakeholders using SQL, Python, or business intelligence tools.
Design and execute advanced analyses for clinical, operational, and policy use cases, including cohort definitions, utilization patterns, quality measurement, and risk stratification.
Develop and document business rules for data standardization, mapping, and transformation to support consistent and scalable analytics.
Support data validation, testing, and quality assurance for data pipelines and analytics-ready datasets, identifying anomalies and supporting root cause analysis.
Translate analytic findings into clear, compelling narratives and data visualizations in Qlik/ AWS Quicksight that support decision-making by technical and non-technical stakeholders.
Monitor key data health and performance metrics and support anomaly detection and ongoing data quality improvements.
Support data strategy and architecture decisions through domain-informed analysis, metric modeling, and collaboration with engineering and product teams.
Mentor junior analysts or cross-functional team members on best practices in data analysis, documentation, and data quality.