Senior Data Scientist

As a Senior Data Scientist, you will play a pivotal role in advancing Reify Health’s data-driven solutions for clinical trials. In this position, you will drive the development of statistical models and machine learning algorithms to improve patient enrollment and trial management. You’ll work in a highly regulated healthcare data environment, ensuring compliance with privacy standards while innovating on predictive analytics. This role involves close collaboration with cross-functional teams (especially ML Engineering) to translate complex data insights into practical, impactful tools for the clinical research community.

• Site Randomization Forecasting: Develop/enhance forecasting models for site randomization and enrollment trends, enabling better planning and resource allocation across trial sites. 

• Patient Matching/Ranking Algorithms: Support projects to build algorithms that intelligently match patients to (or rank patients for) appropriate clinical trials, enhancing recruitment efficiency and patient inclusion. 

• Develop Other Advanced Statistical Models: Create and refine predictive models (Bayesian inference, regression analysis, time-series forecasting) to address other key clinical trial challenges and improve decision-making. 

• AI Monitoring and Bias Detection: Implement processes to monitor machine learning models in production, detecting bias or performance drift and ensuring models remain fair, accurate, and compliant. 

• Data Pipeline & Tooling Development: Build and optimize data pipelines and analytical workflows using tools like AWS Athena, Redshift, SageMaker, and dbt, enabling scalable model training and deployment. 

• Regulatory Compliance in Data Science: Ensure all data science practices align with HIPAA, GDPR, and other privacy regulations, integrating compliance considerations into model development and data handling. 

• Cross-Functional Collaboration: Work closely with machine learning engineers, product managers, and other stakeholders to integrate models into products and clearly communicate insights and recommendations. 

• Education: Master’s or Ph.D. in Statistics, Data Science, Computer Science, or a related quantitative field (or equivalent professional experience). 

• Experience: 5+ years of hands-on data science or analytics experience, preferably in a healthcare, clinical research, or other highly regulated data environment.

• Statistical & ML Expertise: Strong foundation in statistical modeling and machine learning techniques, including experience with Bayesian methods, regression analysis, and time-series forecasting. 

• Model Monitoring & Fairness: Proficiency in evaluating model performance and bias, with the ability to implement AI monitoring tools and bias mitigation strategies to ensure ethical and reliable outcomes. 

• Technical Toolset: Advanced programming skills in Python (with libraries such as scikit-learn, PyMC, mlforecast, etc.) and SQL, as well as familiarity with data transformation tools like dbt. 

• Cloud & Data Infrastructure: Hands-on experience with cloud-based analytics and ML services, especially AWS tools (Athena for querying, Redshift for data warehousing, SageMaker for model development/deployment). 

• Regulated Data Handling: Experience working with sensitive healthcare or clinical trial data under regulations like HIPAA and GDPR, demonstrating a deep commitment to data privacy and security best practices. 

• Collaborative Communication: Excellent teamwork and meticulous verbal/written communication abilities, with a track record of partnering with engineering and product teams to translate data science work into actionable business solutions. 

• Domain Knowledge: Understanding of clinical research or health-tech environments is highly valuable, including insight into clinical trial operations and a passion for improving patient outcomes through data.