Bulldogjob
Praca zdalna
Senior
Senior Data Scientist - LLM / Clinical NLP
⚲ Warsaw
Do uzgodnienia
Wymagania
- SQL
- Hugging Face
- PySpark
- AWS
Opis stanowiska
About the Company
We are an AI-native data and technology partner for private capital and healthcare. Founded in 2010 and headquartered in Warsaw, we work with leading PE firms, VC funds, and healthcare organizations to build proprietary data infrastructure, deploy AI solutions, and drive AI-native transformation. Our clients manage a cumulative $1.2T+ in assets. Our average engagement runs five years. Our NPS sits above 80.
The Opportunity
Our client is a US healthcare data company. They turn real-world EHR data from specialty registries (ophthalmology, urology, oncology) into research-grade data products used for real-world evidence. They are bringing a clinical-data extraction product to market and need extra senior capacity in their Data & Science team. The core problem: getting reliable, structured facts out of messy clinical notes with language models, and proving they are reliable. Your outputs feed research studies and commercial data products, so accuracy and explainability matter as much as shipping. You'll join the client's Data & Science team (the role is called Senior Quantitative Scientist on their side) and work every day with their Product and Medical teams on the US East Coast.
What you'll be doing
The core of the work is applying language models to unstructured clinical text: building extraction pipelines, measuring how well they work, and taking them to production. You'll work with Medical and Product to turn clinical questions into concrete model requirements, and explain results to people who don't read confusion matrices. We're looking for someone who combines research depth with production instinct.
Your Responsibilities
- Build and improve LLM- and transformer-based pipelines that extract structured data from clinical notes: named entity recognition, text classification, relation extraction
- Evaluate models rigorously: gold-standard datasets, error analysis, metrics that clinicians and researchers trust
- Fine-tune, deploy and monitor models in production
- Work with Medical and Product teams to turn clinical questions into concrete data and model requirements
- Present results and trade-offs clearly to technical and non-technical audiences
We are an AI-native data and technology partner for private capital and healthcare. Founded in 2010 and headquartered in Warsaw, we work with leading PE firms, VC funds, and healthcare organizations to build proprietary data infrastructure, deploy AI solutions, and drive AI-native transformation. Our clients manage a cumulative $1.2T+ in assets. Our average engagement runs five years. Our NPS sits above 80.
The Opportunity
Our client is a US healthcare data company. They turn real-world EHR data from specialty registries (ophthalmology, urology, oncology) into research-grade data products used for real-world evidence. They are bringing a clinical-data extraction product to market and need extra senior capacity in their Data & Science team. The core problem: getting reliable, structured facts out of messy clinical notes with language models, and proving they are reliable. Your outputs feed research studies and commercial data products, so accuracy and explainability matter as much as shipping. You'll join the client's Data & Science team (the role is called Senior Quantitative Scientist on their side) and work every day with their Product and Medical teams on the US East Coast.
What you'll be doing
The core of the work is applying language models to unstructured clinical text: building extraction pipelines, measuring how well they work, and taking them to production. You'll work with Medical and Product to turn clinical questions into concrete model requirements, and explain results to people who don't read confusion matrices. We're looking for someone who combines research depth with production instinct.
Your Responsibilities
- Build and improve LLM- and transformer-based pipelines that extract structured data from clinical notes: named entity recognition, text classification, relation extraction
- Evaluate models rigorously: gold-standard datasets, error analysis, metrics that clinicians and researchers trust
- Fine-tune, deploy and monitor models in production
- Work with Medical and Product teams to turn clinical questions into concrete data and model requirements
- Present results and trade-offs clearly to technical and non-technical audiences
🔍 Dekoder Ogłoszenia
✓ Ogłoszenie wygląda transparentnie — brak typowych czerwonych flag.