# Applied Healthcare Researcher

**Company:** [Protege](https://hotfix.jobs/companies/protege)
**Location:** Remote
**Role:** Data Science
**Experience:** 3+ years
**Skills:** Python, SQL, Machine Learning, LLMs, Healthcare Data, Ehr, Emr, Claims Data, Clinical Notes, Classification, Prediction, Fine-Tuning, Data Quality, Dataset Evaluation, Representativeness Analysis
**Posted:** 2026-08-06

> Conducts customer-facing applied research to determine whether healthcare datasets can support AI model development and evaluation. The role combines ML/LLM method development, healthcare data analysis, feasibility studies, and collaboration with technical and data partnerships teams.

## Job Description

## Responsibilities

### Customer Research Partnership
- Serve as the primary technical and research point of contact for healthcare customer conversations.
- Translate model-development goals into concrete, feasible data strategies.
- Help customers scope opportunities and identify high-value healthcare data.
- Explain data limitations, tradeoffs, and potential biases to technical stakeholders.
- Answer customers’ research questions about delivered data.

### Applied Research and Method Development
- Develop and evaluate methods such as fine-tuning, LLM-based extraction, classification, and rules-based approaches.
- Design and run pre-contract feasibility research to assess whether data supports model objectives.
- Build evidence through benchmarks, validation analyses, error characterization, and assessments of data limitations.
- Partner on healthcare benchmarks across modalities.

### Data Feasibility and Dataset Strategy
- Evaluate whether requested variables, labels, and cohort definitions are achievable with available healthcare data.
- Identify proxy variables and alternative dataset structures.
- Analyze partner and source datasets for schema, field availability, quality, completeness, and required transformations.
- Help evaluate data partners and identify valuable datasets.

### Reusable Research and Scaling
- Produce reusable research, evidence, and technical collateral.
- Identify opportunities to turn successful approaches into repeatable workflows with Product and Engineering.
- Expand proven healthcare datasets across multiple customers.

### Cross-Functional Collaboration
- Work with Solutions and Forward Deployed Engineering teams on opportunities.
- Coordinate with Healthcare Data Partnerships, Product, and Engineering.

## Requirements
- Advanced degree (PhD or master’s plus 3+ years of industry experience) in machine learning, computer science, biomedical informatics, epidemiology, statistics, or a related quantitative field, or equivalent applied experience.
- Hands-on experience building and evaluating ML or LLM-based systems for extraction, classification, or prediction on real-world data.
- Experience with healthcare data such as claims, EMR/EHR, clinical notes, imaging, or registries.
- Strong Python and SQL skills, with the ability to work independently against large datasets.
- Experience designing evaluations that measure data quality and dataset representativeness.
- Ability to work directly with technical stakeholders and translate ambiguous goals into concrete research plans.
- Comfort operating on customer timelines while maintaining research rigor.

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