Data Scientist – Science Validation

At Generation Lab, we're designing the tools to reverse human aging.

We believe scientific breakthroughs must be statistically rigorous, reproducible, and operationally robust. Our biological age platform depends not only on innovation—but on precision, validation, and defensibility.

If you're a scientist who values rigor over hype, reproducibility over shortcuts, and robust systems over fragile ones—we want to work with you.

Generation Lab is an AI-powered longevity company building the world's most advanced biological age platform.

System Age measures the health of 19 organismal systems from a single at-home blood draw. Behind every result is a foundation of statistical modeling, quality control, and validation infrastructure.

As we scale, scientific robustness becomes as important as scientific innovation.

We're hiring a Data Scientist to execute scientific validation, reproducibility analysis, and analytical robustness for our platform.

This is not a primary research role to start.

Instead, this role ensures that our science is statistically defensible, reproducible across batches and cohorts, operationally reliable, and ready for clinical-grade scrutiny.

You will work closely with the Senior Data Scientist and CSO to execute validation studies, investigate anomalies, and strengthen quality control systems as we scale.

Validation & Reproducibility

• Execute structured validation analyses across internal and public datasets.
• Quantify technical replicate consistency and batch-level variance.
• Evaluate normalization strategies and reference curve behavior.
• Analyze shipping, temperature, and collection method stability.
• Establish statistical acceptance thresholds for performance metrics.
• Implement and maintain CpG-level and system-level QC checks.
• Investigate anomalous outputs and batch-level deviations.
• Reduce manual QC investigation through automation.
• Document statistical rationale behind cutoff logic and validation decisions.

Analytical Investigation & Support

• Independently investigate production-level anomalies and rerun analyses when needed.
• Resolve the majority of validation-related analytical inquiries without escalation.
• Clearly communicate statistical findings to product and operations teams.
• Escalate only when findings indicate potential methodological redesign.

Clinical-Grade Validation (Growth Area)

• Contribute to structured validation documentation.
• Help define reproducibility, precision, and bias metrics aligned with clinical diagnostic standards.
• Support long-term validation infrastructure as the platform evolves toward regulated environments.

• Have strong experience in Python or R (ideally both).
• Are comfortable working with high-dimensional biological data (100K–1M+ features).
• Have a solid foundation in statistical modeling and variance analysis.
• Think carefully about bias, reproducibility, and robustness.
• Enjoy structured validation and operational problem-solving.
• Write clean, reproducible, well-documented code.
• Can explain statistical findings clearly to non-technical stakeholders.
• Have experience with methylation array or sequencing data.
• Have worked in clinical research, epidemiology, translational biostatistics, or assay validation.
• Have familiarity with normalization workflows and QC frameworks.
• Have experience in documentation-heavy or regulated environments.
• Enjoy building reliable systems in fast-moving startups.

• Work at the intersection of aging science, AI, and biological measurement.
• Help ensure the scientific credibility of a rapidly scaling longevity platform.
• Build systems that make biological age measurement defensible and reproducible.
• Competitive salary and equity.
• SF Bay Area preferred; remote considered for exceptional candidates.