Data Scientist

Function Health•Canada, KS

About The Position

Function Health’s mission is to empower people to live longer, healthier lives through proactive, data-driven healthcare. We aggregate and analyze comprehensive health data - including imaging, laboratory results, longitudinal biomarkers, and other health signals - to deliver clear, clinically meaningful insights. We believe individuals should understand their health deeply, in context, and over time. Our platform brings together complex medical data and transforms it into trustworthy, actionable information that supports better health decisions. As a Data Scientist, you will focus on the clinical interpretation, analysis, and contextualization of complex, multi-modality healthcare data, while playing a key role in turning that data into insights that inform models, products, and decisions across the organization. You will work at the intersection of clinical, engineering, and AI teams - translating between medical context, data realities, and production systems. This role bridges not only raw healthcare data and downstream ML systems, but also the teams and platforms that depend on that data, ensuring it is clinically sound, well-structured, and meaningfully interpretable. You will help ensure that Function’s data is not just available, but used well - to support modeling, guide product decisions, surface operational issues, and build trustworthy, data-driven healthcare experiences. You will be working with the R&D and AI engineering teams and reporting to Radhika Tibrewala.

Requirements

5+ years of experience in data science, analytics, or applied research, preferably in healthcare, life sciences, or biomedical domains.
Strong proficiency in Python and SQL for data analysis and dataset preparation.
Experience preparing datasets for machine learning or statistical modeling.
Experience working with clinical, lab, or biomedical data.
Experience working with messy, real-world data from multiple sources or vendors.
Ability to communicate clearly with clinicians, engineers, and non-technical stakeholders.
Familiarity with statistical methods, exploratory data analysis, and basic modeling techniques.

Nice To Haves

Background in biology, medicine, public health, bioinformatics, or related fields.
Experience with multi-modal or imaging-derived data.
Experience supporting ML model development or validation in production environments.
Familiarity with PHI-sensitive and regulated data environments.

Responsibilities

Analyze and interpret multi-modality health data, including blood tests, biomarkers, imaging-derived features, and longitudinal patient histories.
Apply clinical reasoning to identify meaningful patterns, trends, and risk signals across time and modalities.
Prepare, curate, and validate analysis-ready and ML-ready datasets for downstream model training and evaluation.
Define clinically meaningful features, labels, cohorts, and outcome variables in partnership with clinicians and ML engineers.
Use data to inform decisions beyond modeling, such as evaluating data fitness, identifying gaps or inconsistencies, and supporting product or operational questions.
Investigate and resolve data quality issues through a clinical lens (e.g., implausible lab values, inconsistent mappings, unexpected distribution shifts).
Collaborate with data engineers to shape data models and transformations that support clinical interpretation and ML workflows.
Support model validation by helping interpret model inputs, outputs, and edge cases in a clinically responsible way.
Translate complex analyses and modeling inputs into clear documentation and narratives for internal teams.
Contribute to best practices around clinically grounded data usage and preparation across the organization.