Staff Data Scientist - Longitudinal Methodology & Algorithm Dev

CareDx, Inc. is a leading precision medicine solutions company focused on the discovery, development, and commercialization of clinically differentiated, high-value healthcare solutions for transplant patients and caregivers. CareDx offers products, testing services, and digital healthcare solutions along the pre- and post-transplant patient journey, and is the leading provider of genomics-based information for transplant patients.

The data science team is dedicated to the discovery, development, and delivery of complex genomic clinical insights. The team also drives innovations, new machine-learning-driven products, and enhancements to existing products, all aimed at advancing transplant patient care. As a Staff Data Scientist, under the guidance of the Senior Director of Data Science, you will work independently and cross-functionally, engaging with R&D, data engineering, biostatistics, bioinformatics teams to design, build, and deploy innovative data solutions.

Responsibilities:

• Partner closely with R&D and medical scientists to deeply understand the scientific and clinical context of transplant-related projects, ensuring data science strategies are aligned with product and research goals.
• Lead product development efforts from a data science perspective-translating target product profiles into analytical and modeling strategies, defining success metrics, and driving cross-functional collaboration throughout the development lifecycle.
• Design, implement, and validate machine learning and optimization models to derive actionable insights from diverse data sources, including molecular biomarkers, clinical trial datasets, electronic medical records (EMR), and digital health solution data.
• Research and develop novel algorithms and approaches to improve model performance, interpretability, and clinical utility.
• Own the end-to-end data science workflow-from data exploration and model development to deployment-ensuring reproducibility, scalability, and robust documentation.
• Communicate key findings and insights through effective data storytelling, clear and concise communication, and compelling visualizations that resonate with technical and non-technical stakeholders alike.
• Foster a collaborative data science culture by sharing best practices, mentoring team members, and promoting a high standard of analytical rigor and integrity.

Qualifications:

• PhD or Master's degree in Biostatistics, Epidemiology, Bioinformatics, or a quantitative discipline with 3+ (PhD) pr 6+ (Master) years of industry experience in applied data science, preferably in healthcare, biotech, or life sciences. The title and seniority level may be adjusted based on the candidate's qualifications and experience.
• Strong foundation in longitudinal data analysis, with hands-on experience in: GEE, linear and nonlinear mixed-effects models. Familiarity and understanding of real-world challenges in longitudinal studies (e.g., missing data, informative dropout).
• Experience in time-to-event analysis, such as survival, competing risk and multistate models. Familiarity in joint modeling of longitudinal and time-to-event data, dynamic landmark models.
• Familiarity with machine learning approaches adapted for survival and time-series data: random Survival Forests, time-varying risk models, and survival boosting methods
• Proficiency in evaluating time-to-event models using appropriate metrics (e.g., C-index, calibration curves, time-dependent AUC).
• Experience analyzing complex healthcare datasets, including registries, EMRs, clinical trials, molecular biomarkers, and digital health streams.
• Solid grounding in classical machine learning approaches, including regularized regression (e.g., Elastic Net), random forests, and ensemble learning, SVM and gradient boosting, cross-validation techniques and model tuning strategies
• Solid understanding in quantitative evaluation in both diagnostic and prognostic studies.
• Proficiency in R (e.g., survival, lme4, nlme, gee, randomForestSRC, riskRegression) and/or Python (e.g., lifelines, scikit-survival, statsmodels, scikit-learn, xgboost).
• Strong communication skills with the ability to present complex analytical concepts to cross-functional teams.
• Commitment to reproducibility and collaborative science through version control, well-documented workflows, and transparent reporting.

Preferred Qualifications:

• Fluency in generative AI (GenAI) tools and workflows, with a strong ability to evaluate, adapt, and integrate emerging technologies into daily work.
• Demonstrated experience using AI-assisted coding tools (e.g., GitHub Copilot) to enhance productivity, code quality, and scalability.
• Proven track record of staying current with cutting-edge developments in AI/ML and proactively incorporating relevant tools into data science workflows.
• Ability to lead or contribute to a continuous evaluate-adapt-deploy cycle for technology adoption within the team.

Additional Details:

Every individual at CareDx has a direct impact on our collective mission to improve the lives of organ transplant patients worldwide. We believe in taking great care of our people, so they take even greater care of our patients.

Our competitive Total Rewards package includes:

• Competitive base salary and incentive compensation
• Health and welfare benefits including a gym reimbursement program
• 401(k) savings plan match
• Employee Stock Purchase Plan
• Pre-tax commuter benefits
• And more!

In addition, we have a Living Donor Employee Recovery Policy that allows up to 30 days of paid leave annually to a full-time employee who makes the selfless act of donating an organ or bone marrow.

With products that are making a difference in the lives of transplant patients today and a promising pipeline for the future, it's an exciting time to be part of the CareDx team. Join us in partnering with transplant patients to transform our future together.

CareDx, Inc. is an Equal Opportunity Employer and participates in the E-Verify program.

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