Senior Data Scientist

Accountabilities:: As a Senior Data Scientist, you will lead the design, implementation, and optimization of secure, scalable data systems that support healthcare analytics, clinical workflows, and value-based care initiatives. You will work closely with technical and business stakeholders to translate complex requirements into robust data solutions while ensuring data quality, regulatory compliance, and long-term maintainability. Design, architect, build, and optimize reliable, scalable, and secure data infrastructure to support healthcare applications and analytics. Develop and maintain foundational healthcare data models and schemas, translating clinical, operational, product, and reporting requirements into scalable data structures. Apply healthcare data standards and common data models, including TUVA, OMOP, and FHIR, to establish consistent and interoperable data systems. Build and maintain production-grade data pipelines to ingest, process, enrich, normalize, and transform large volumes of healthcare data. Map raw healthcare data into standardized models, resolving data quality issues, terminology differences, normalization challenges, and structural inconsistencies. Establish data quality standards, validation frameworks, monitoring systems, and governance practices to maintain data accuracy, completeness, consistency, and integrity. Optimize data processing and retrieval through query tuning, indexing, partitioning, and other performance-enhancement techniques. Design and deploy secure, scalable cloud infrastructure using infrastructure-as-code principles and cloud-native development practices. Ensure healthcare data systems comply with applicable security and regulatory requirements, including HIPAA and relevant HITRUST standards. Develop and maintain direct, bidirectional integrations with electronic medical record and electronic health record platforms. Implement and manage data warehouse and data lake solutions that support reliable storage, processing, reporting, and downstream applications. Apply workflow orchestration, automated testing, CI/CD, and DevOps practices to improve the reliability and efficiency of data deployments. Make independent, high-impact technical decisions while collaborating constructively with engineering and other stakeholders to solve complex problems. Help establish the technical foundations, architecture, and engineering standards required to support future product development and organizational growth. Requirements: You bring extensive hands-on experience building healthcare data systems and designing scalable data architectures. You understand the complexity of clinical data, interoperability, and regulatory compliance, and you can translate diverse business and clinical requirements into reliable technical solutions. You are comfortable taking ownership in an early-stage environment, balancing architectural quality with practical execution, and making thoughtful decisions about system design, performance, and security. At least 8 years of dedicated professional experience in data science or data engineering, preferably in a startup or fast-paced healthcare technology environment. Deep, hands-on experience working with healthcare data standards and common data models, including TUVA, OMOP, or FHIR. Demonstrated experience designing foundational data models and schemas from scratch and translating clinical, business, product, and user requirements into scalable structures. Proven ability to map raw healthcare data into standardized models and resolve data quality, terminology, normalization, and structural inconsistencies. Direct experience integrating with EMR/EHR platforms, ideally including eClinicalWorks, AthenaOne, Epic, or comparable systems. Knowledge of healthcare data regulations and security requirements, including HIPAA and HITRUST, as well as healthcare data formats such as FHIR. A bachelor's degree in Computer Science, Software Engineering, or a related field, or equivalent professional experience. Expert proficiency in SQL and at least one programming language used for data processing, such as Python or Scala. Extensive experience designing and managing data warehouses, such as Snowflake, Google BigQuery, or AWS Redshift. Experience with data lake technologies, including Amazon S3 or Delta Lake. Hands-on experience with big data processing frameworks such as Apache Spark or Dask. Strong understanding of cloud platforms, preferably AWS, including services such as S3, Lambda, and RDS. Practical experience with workflow orchestration tools such as Apache Airflow or AWS Step Functions. Deep knowledge of system architecture, scalability, data reliability, and performance optimization. Experience with test automation, continuous integration and continuous delivery (CI/CD), and DevOps best practices for data deployments. Strong problem-solving skills, technical judgment, and the ability to work independently while making high-impact architectural decisions. Legal authorization to work in the United States without requiring employer sponsorship now or in the future. Benefits: Competitive salary and equity participation in an early-stage startup. Opportunity to define and build the technical solution and data architecture from the ground up. Potential for a fully remote work environment, with flexible working hours. Health benefits. Learning stipends and professional development support. A collaborative, transparent culture that values creativity, technical ownership, and initiative. The opportunity to contribute to better cardiovascular outcomes and more cost-effective healthcare. Compensation may vary based on location, role level, relevant skills, experience, and market conditions. Additional incentives or benefits may apply depending on the position. Additional Information: Location: Pittsburgh is preferred, but remote work is available for eligible candidates based in the United States. Employment type: Full-time. The organization is committed to equal employment opportunities and an inclusive workplace. Employment decisions are based on qualifications, merit, and business needs, without discrimination based on legally protected characteristics.