Senior Data Scientist

OneStudyTeam

11d•$140,000 - $190,000•Remote

About The Position

At OneStudyTeam (a Reify Health company), we specialize in speeding up clinical trials and increasing the chance of new therapies being approved with the ultimate goal of improving patient outcomes. Our cloud-based platform, StudyTeam, brings research site workflows online and enables sites, sponsors, and other key stakeholders to work together more effectively. StudyTeam is trusted by the largest global biopharmaceutical companies, used in over 6,000 research sites, and is available in over 100 countries. Join us in our mission to advance clinical research and improve patient care. One mission. One team. That’s OneStudyTeam. 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.

Requirements

Minimum of Master’s or Ph.D. in Statistics, Data Science, Computer Science, or a related quantitative field (or equivalent professional experience).
Minimum of 5+ years of hands-on data science or analytics experience, preferably in a healthcare, clinical research, or other highly regulated data environment.
Strong foundation in statistical modeling and machine learning techniques, including experience with Bayesian methods, regression analysis, and time-series forecasting.
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.
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.
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).
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.
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.

Nice To Haves

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.

Responsibilities

Develop/enhance forecasting models for site randomization and enrollment trends, enabling better planning and resource allocation across trial sites.
Support projects to build algorithms that intelligently match patients to (or rank patients for) appropriate clinical trials, enhancing recruitment efficiency and patient inclusion.
Create and refine predictive models (Bayesian inference, regression analysis, time-series forecasting) to address other key clinical trial challenges and improve decision-making.
Implement processes to monitor machine learning models in production, detecting bias or performance drift and ensuring models remain fair, accurate, and compliant.
Build and optimize data pipelines and analytical workflows using tools like AWS Athena, Redshift, SageMaker, and dbt, enabling scalable model training and deployment.
Ensure all data science practices align with HIPAA, GDPR, and other privacy regulations, integrating compliance considerations into model development and data handling.
Work closely with machine learning engineers, product managers, and other stakeholders to integrate models into products and clearly communicate insights and recommendations.