Data Scientist Analyst
Role summary
MedReview is seeking a Data Scientist/Analyst in Austin, Texas (remote considered) to build and maintain a scalable feature store supporting machine learning initiatives. This role bridges data analysis, feature engineering, and MLOps, ensuring feature accessibility for model training and real-time deployment. The ideal candidate will have 3+ years of experience in data science, data engineering, or MLOps, with a focus on data infrastructure and ML workflows, and 1+ years of healthcare industry experience. Proficiency in Python, R, SQL, and data processing frameworks like Spark is required. Familiarity with feature store technologies and cloud platforms is essential for enhancing the company's AI and ML capabilities in healthcare.
About The Company
MedReview is a leading organization dedicated to transforming healthcare data management and analytics. Our mission is to leverage innovative technology solutions to improve clinical decision-making, optimize healthcare operations, and enhance patient outcomes. With a focus on data-driven insights, we serve a diverse range of clients across the healthcare industry, including hospitals, insurance providers, and healthcare technology companies. Our team is committed to fostering a collaborative environment where innovation, integrity, and excellence are at the core of everything we do. As part of our growth strategy, we continuously invest in cutting-edge tools and talent to stay ahead in the rapidly evolving healthcare landscape.
About The Role
We are seeking a talented Data Scientist/Analyst to join our dynamic team in Austin, Texas. The ideal candidate will play a pivotal role in building and maintaining a scalable, robust feature store that supports our machine learning initiatives. This position requires a highly organized and self-motivated professional capable of working in a fast-paced, global environment. The successful candidate will act as a bridge between data analysis, feature engineering, and MLOps, ensuring that features are discoverable, consistent, and readily accessible for both model training and real-time deployment. While the primary location is Austin, Texas, we are open to remote candidates for the right fit. This role offers an exciting opportunity to contribute directly to the enhancement of our AI and machine learning capabilities, impacting healthcare outcomes on a broad scale.
Qualifications
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related quantitative field
- 3+ years of experience in data science, data engineering, or MLOps roles, with a focus on data infrastructure and machine learning workflows
- 1+ years of experience in the healthcare industry or familiarity with healthcare claims, coding, and clinical decision-making
- Strong programming skills in Python, R, and SQL
- Experience with data processing frameworks such as Spark, Flink, or Airflow
- Hands-on experience with feature store technologies like Feast, SageMaker Feature Store, Tecton, or custom implementations (preferred)
- Familiarity with cloud platforms such as AWS, Azure, or GCP and related data services
- Knowledge of machine learning fundamentals, statistical modeling, and data visualization tools
- Familiarity with Clickhouse or similar technologies (a plus)
- Experience working in an Agile environment
Responsibilities
- Collaborate with ML engineers and data platform teams to design, implement, and maintain the architecture of the feature store for both offline (batch) and online (real-time) use cases
- Identify, define, and engineer high-quality features from diverse raw data sources, utilizing statistical analysis and domain expertise
- Partner with database administrators, clinical experts, data scientists, ML engineers, and business stakeholders to promote feature reuse, define governance standards, and ensure data consistency across models
- Build and optimize data ingestion and transformation pipelines using distributed data frameworks to ensure data accuracy, reliability, and freshness in the feature store
- Generate training and testing datasets from the feature store and support ML engineers in seamless feature serving for model inference in production environments
- Develop monitoring and alerting frameworks to track data quality, integrity, and latency, proactively resolving issues as they arise
- Document feature definitions, data sources, and best practices; communicate technical concepts effectively to both technical and non-technical audiences
Benefits
- Competitive salary package aligned with industry standards
- Health, dental, and vision insurance coverage
- Flexible work arrangements including remote work options
- Opportunities for professional growth and development through training and conferences
- Collaborative and innovative work environment with a talented team
- Participation in cutting-edge healthcare technology projects with real-world impact
Equal Opportunity
MedReview is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, age, disability, or any other protected status under applicable law.
