Clinical Data Specialist

Function Health•Canada, KS

About The Position

Function Health is seeking a Clinical Data Specialist to be responsible for the statistical design, validation, and analytical rigor underpinning Function Health’s machine learning models and clinical insights. This role involves close collaboration with Engineering, Data, and Clinical teams to ensure models are evaluated with appropriate statistical methods, validated across relevant populations, and supported by transparent, defensible analysis suitable for healthcare and regulated environments. The work will focus on defining statistical analysis plans, assessing model performance and reliability, and translating complex results into clinically meaningful evidence. Function Health is redefining how individuals understand, measure, and improve their health by moving beyond the limitations of traditional care and enabling comprehensive, continuous insight into human biology. The company has been recognized as one of Fast Company’s Most Innovative Companies of 2024 and is venture-backed by Redpoint and other leading venture firms, with over half a million members. Through comprehensive lab testing, MRI and CT imaging, longitudinal data, and guidance from AI and clinical teams, Function gives members a complete and continuous view of their health — and the clarity to act on it. Function is building an integrated health platform that spans the full path from testing to health clarity to daily action. The company recently announced a $298M Series B and is entering its next chapter of growth. As we scale, the quality and durability of our People systems, data, and insights will directly shape our ability to attract, retain, and support exceptional talent. We are growing our team and seeking out world-class talent that deeply believes in our mission to positively impact global health, has a relentless bias toward action, and a growth mindset. Function fosters a collaborative and dynamic environment where every day we build the future.

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 (e.g., NumPy, pandas, SciPy, statsmodels); experience working alongside ML workflows.
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 skills in interpreting results, assessing uncertainty, and communicating limitations.
Ability to collaborate effectively 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 techniques.
Familiarity with model evaluation concepts such as calibration, discrimination, and decision-curve analysis.
Exposure to fairness, bias assessment, and subgroup performance analysis.
Experience working with PHI-sensitive data and compliance-driven environments.
Advanced degree (MS or PhD) in biostatistics, statistics, epidemiology, public health, or a related field.

Responsibilities

Design and own statistical analysis plans (SAPs) to validate machine learning models, including definitions of cohorts, endpoints, metrics, and subgroup analyses.
Develop and execute rigorous statistical evaluations of ML models, including performance assessment, calibration analysis, uncertainty quantification, and sensitivity analyses.
Lead subgroup, bias, and fairness analyses to understand model behavior across demographics, clinical characteristics, and longitudinal trajectories.
Partner with ML scientists to define appropriate validation strategies, including cross-validation, temporal validation, external validation, and robustness testing.
Apply classical and modern statistical methods (e.g., regression, survival analysis, mixed-effects models) to contextualize and validate model outputs.
Analyze longitudinal health data to assess change over time, progression patterns, and stability of model predictions in the presence of missingness and real-world noise.
Conduct literature review and methodological research to ensure analyses align with clinical standards and best practices in biostatistics and epidemiology.
Document analytical assumptions, limitations, and results clearly to support auditability, interpretability, and regulatory readiness.
Communicate findings and statistical rationale to clinicians, product leaders, and engineers, translating technical results into clear clinical insights.