Senior Data Scientist

• Strong foundation in statistics and Machine Learning, with the ability to reason confidently about distributions, probability, model behavior, and evaluation methodology.
• Strong Python skills and ability to write clean, maintainable analytical and production-oriented code.
• Hands-on experience designing and performing statistical analyses on real-world datasets.
• Solid understanding of model evaluation, confidence calibration, statistical testing, and performance metrics.
• Experience analyzing data distributions, identifying outliers, and defining meaningful thresholds and quality indicators.
• Understanding of data drift, model degradation, and distributional monitoring concepts.
• Ability to design evaluation approaches where ground truth may be incomplete, noisy, or domain-specific.
• Good understanding of Machine Learning workflows and the relationship between data, models, evaluation, and production systems.
• Ability to understand application logic, system behavior, and data flow beyond isolated analytical notebooks.
• Strong analytical and problem-solving skills with the ability to independently scope ambiguous problems and turn them into structured analyses.
• Ability to clearly communicate methodology, assumptions, findings, and limitations to both technical and non-technical stakeholders.
• Strong ownership mindset and ability to collaborate effectively within a small cross-functional team.
• Professional working proficiency in English.

• Experience with Generative AI, LLM evaluation, or AI safety/quality assessment.
• Experience evaluating embeddings, vector search, retrieval systems, or RAG pipelines.
• Familiarity with Databricks, Spark, or other large-scale/batch data processing technologies.
• Experience using AI coding agents or assistants as part of the development and analytical workflow, with the ability to critically validate their output.
• Experience working with clinical or healthcare data.
• Familiarity with healthcare standards such as FHIR or HL7.
• Experience in Healthcare, Digital Health, or another regulated industry.
• Exposure to production ML monitoring, experimentation, or evaluation infrastructure.

We are looking for a Data Scientist to join a Digital Health team building an AI-powered platform that helps people better understand and navigate their health.

You will work at the intersection of Machine Learning, statistics, clinical data, and Generative AI, focusing on how AI system quality, safety, and reliability are measured in production.

This role is ideal for a Data Scientist with strong statistical and ML fundamentals, solid Python skills, and the ability to independently investigate complex, not-yet-fully-defined analytical problems.

The product is a clinical AI platform that combines LLM orchestration, healthcare data, clinical capabilities, and AI safety mechanisms to deliver personalized healthcare experiences.

The Data Science team develops and evaluates the intelligence behind the platform. A major focus is establishing rigorous methods for understanding how the system behaves over time — including drift detection, confidence calibration, safety-weighted quality scoring, and comparison against clinical ground truth.

You will also contribute to health scoring analytics, evaluate retrieval and embedding quality as RAG capabilities evolve, and analyze the behavior and accuracy of clinical AI capabilities.

You will work closely with experienced Data Scientists and an ML Platform Engineer responsible for operationalizing evaluation and monitoring infrastructure.

• Own the analytical side of ML/AI evaluation and monitoring, including drift thresholds, distributional analysis, and quality metrics.
• Develop statistical methods for safety-weighted quality scoring and confidence calibration.
• Compare AI and model outputs against clinical ground truth and evaluate performance across different populations and scenarios.
• Perform population percentile, distribution, and outlier analysis supporting health scoring capabilities.
• Evaluate retrieval quality, embeddings, and vector search performance as RAG capabilities evolve.
• Analyze usage patterns, overlap, deduplication signals, and classification accuracy across clinical AI capabilities.
• Partner with Data Scientists and ML Platform Engineers to translate analytical methods into repeatable production evaluation workflows.
• Investigate unexpected model or system behavior and identify statistically meaningful changes or degradation.
• Use Python and modern AI-assisted development tools to accelerate analysis while maintaining strong technical validation and engineering quality.
• Clearly document evaluation methodology, assumptions, results, and recommendations.