Clinical Data Specialist

Responsibilities

• Design and own statistical analysis plans (SAPs) for validating machine learning models, including cohorts, endpoints, metrics, and subgroup analyses.
• Conduct rigorous model evaluations covering performance, calibration, uncertainty quantification, sensitivity analyses, cross-validation, temporal and external validation, and robustness testing.
• Lead subgroup, bias, and fairness analyses across demographic and clinical populations and longitudinal trajectories.
• Apply regression, survival analysis, mixed-effects models, and other statistical methods to contextualize and validate model outputs.
• Analyze longitudinal health data, including change over time, progression patterns, prediction stability, missingness, censoring, and time-dependent effects.
• Research relevant clinical standards and biostatistical and epidemiological methods.
• Document assumptions, limitations, methods, and results for auditability, interpretability, and regulatory readiness.
• Communicate statistical rationale and findings to clinicians, product leaders, engineers, and machine learning scientists.

Requirements

• 3+ years of experience in biostatistics, applied statistics, epidemiology, or a related quantitative role.
• Strong foundation in statistical inference, experimental design, and model validation.
• Proficiency in Python and statistical libraries such as NumPy, pandas, SciPy, and statsmodels.
• Experience designing and executing statistical analysis plans for complex datasets.
• Familiarity with longitudinal and real-world data, including missingness, censoring, and time-dependent effects.
• Strong ability to interpret results, assess uncertainty, and communicate limitations.
• Ability to collaborate with machine learning, engineering, and clinical teams.

Nice-to-haves

• Experience validating or supporting machine learning models in healthcare or other regulated domains.
• Background in survival analysis, causal inference, or longitudinal modeling.
• Familiarity with calibration, discrimination, and decision-curve analysis.
• Experience with fairness, bias assessment, and subgroup performance analysis.
• Experience working with PHI-sensitive data and compliance-driven environments.
• Advanced degree in biostatistics, statistics, epidemiology, public health, or a related field.

Compensation and Benefits

• Competitive salary and benefits package.
• Flexible working hours.