TL;DR

Data Science Lead (Health AI): Architecting, scaling, and optimizing data pipelines, datasets, and metrics frameworks for a health AI companion with an accent on understanding user behavior, evaluating model performance, and measuring health impact. Focus on translating raw signals into insights to shape product decisions and ensure safe, effective, and evidence-grounded health AI systems.

Location: London, United Kingdom

Company

%hirify_global% is inventing an AI Companion for everyone, focusing on health to help millions of users better understand and proactively manage their wellbeing.

What you will do

Develop and track metrics for Copilot usage and quality in the healthcare category.
Drive product insights, opportunity analysis, and support efforts across Microsoft Copilot.
Enable A/B testing and other experimentation for new features.
Design, build, and maintain high-quality data pipelines and models for analytics, dashboards, and product experimentation.
Partner with product and clinical counterparts to define source-of-truth datasets and standardized metrics.
Contribute to shaping data infrastructure strategy and tooling to support next-generation health AI systems.

Requirements

Bachelor’s or Master’s degree in Mathematics, Statistics, Data Science, Computer Science, Engineering, or related field OR equivalent experience.
Experience leveraging complex data, applied data science, developing sophisticated algorithms, and executing large-scale A/B testing.
Experience with metrics creation, predicting trend analysis, measuring traffic patterns, and assessing experimentation results.
Experience working with Python for data processing, analytics, or pipeline orchestration.
Experience applying data science in a scaled consumer products environment.
Demonstrated interpersonal skills and ability to work closely with cross-functional teams, while clearly communicating complex technical concepts.

Nice to have

Experience working in healthcare or digital health data environments.
Familiarity with typical health data taxonomies, governance, data privacy, and compliance standards (HIPAA & PHI handling, De-identification standards, etc.).
Exposure to large language model (LLM) or generative AI systems, particularly in analytics or model evaluation contexts.

Culture & Benefits

Enjoy working in a fast-paced, design-driven, product development cycle.
Be part of a team crafting something that challenges everything known about software and consumer products.
Collaborate closely with product, engineering, and clinical counterparts.