Healthcare Data Scientist: Cohort Builder & Validation

Role Overview

We are hiring a Solutions Applied Data Scientist to help design, construct, and validate complex healthcare data cohorts used for AI model training. This role sits within the delivery organization, working closely with Solutions Leads and delivery engineers to solve complex data challenges that arise during customer projects.

• Solutions Leads own the customer relationship and overall delivery of projects. The Solutions Applied Data Scientist serves as their technical partner for more complex data problems
  , including cohort construction, multi-source dataset assembly, feasibility analysis, and data validation.
• You will help translate research generated by Protege’s Data Lab and customer requirements into practical dataset definitions, determine whether those requirements can be met with available data, and build the SQL and analysis needed to construct the resulting datasets.
• You will also collaborate with delivery engineers when solutions require changes to data pipelines, infrastructure, or large-scale data movement.
• This is a highly applied role focused on solving real-world dataset challenges
  , not research or model development.

During delivery projects, Solutions Leads may encounter complex data challenges that require deeper analysis or technical problem-solving. You will act as a technical partner
, helping solve things such as:

• Complex cohort definitions that require multi-source joins
• Linking datasets across different data partners
• Investigating unexpected gaps or anomalies in delivered data
• Evaluating whether requested variables or labels exist in available datasets
• Determining whether a dataset can realistically satisfy model requirements

You will work collaboratively with Solutions Leads to unblock delivery challenges while keeping projects moving toward successful completion.

When solutions require infrastructure or pipeline changes, you will partner with the Solutions Engineer and internal platform engineering teams to implement the required workflows.

Work with Solutions Leads to translate customer requirements into concrete dataset logic. You will help ensure that datasets accurately represent the intended population and meet customer specifications.

Responsibilities include:

• Writing complex SQL queries to construct cohorts
• Implementing inclusion and exclusion logic
• Joining datasets across multiple data sources
• Validating linkage between datasets
• Identifying and resolving inconsistencies or missing fields
• Partner with Solutions Leads to resolve complex data questions that arise during project delivery
• Escalate or collaborate with delivery engineers when dataset construction requires pipeline changes or large-scale data processing

Before complex datasets are delivered to customers you will help validate that they meet required standards. You will work closely with Solutions Leads before datasets are delivered to ensure that the datasets meet agreed acceptance criteria. Review bespoke QA methodology and suggest platform improvements to Product and Engineering to decrease custom work across engagements.

Responsibilities include:

• Performing data completeness analysis
• Investigating missing or anomalous data
• Verifying cohort logic results
• Validating row counts and dataset structure
• Creating summary statistics and validation outputs

Many customer projects involve AI researchers who are defining the healthcare datasets required to train or evaluate models. You will work with these customer teams to translate research goals into practical dataset specifications.

Responsibilities include:

• Reviewing dataset requests from AI researchers and model development teams
• Helping clarify and refine requirements for model training or evaluation datasets
• Evaluating whether requested variables or labels exist in available data sources
• Identifying proxy variables or alternative dataset structures when ideal variables are unavailable
• Assessing feasibility of requested cohort definitions given real-world data constraints
• Explaining data limitations, tradeoffs, and potential biases to technical stakeholders
• Iterating with researchers to converge on datasets that are both scientifically meaningful and operationally feasible

Many datasets originate from external healthcare data partners.

You will help analyze partner datasets to:

• Understand schema and field availability
• Assess data quality and completeness
• Identify required transformations
• Evaluate feasibility of cohort logic

As delivery patterns emerge, you will help develop tools and reusable workflows that improve efficiency.

Examples include:

• Reusable SQL templates for cohort construction
• Automated validation checks
• Scripts for dataset preparation
• Tools that reduce manual delivery work

This role is an important bridge between manual dataset delivery and scalable data…